High-Throughput Genomic Profiling of Adult Solid Tumors Reveals Novel Insights Into

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Author Manuscript Published OnlineFirst on February 24, 2017; DOI: 10.1158/0008-5472.CAN-16-2479 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

High-throughput genomic profiling of adult solid tumors reveals novel insights into

cancer pathogenesis

Ryan Hartmaier1,#,*, Lee A. Albacker1,#, Juliann Chmielecki1,#, Mark Bailey1, Jie He1,

Michael E. Goldberg1, Shakti Ramkissoon1, James Suh1, Julia Elvin1, Samuel Chiacchia1,

Garrett M. Frampton1, Jeffrey S. Ross1,2, Vincent A. Miller1, Philip J. Stephens1, Doron

Lipson1*

1Foundation Medicine, Cambridge MA

2Albany Medical College, Albany NY

#These authors contributed equally to this work.

*To whom correspondence should be addressed: [email protected]

or [email protected]

Running title: Public release of adult cancer genomic data

Conflicts of interest: All authors are employees of and equity holders in Foundation

Medicine, Cambridge MA.

Downloaded from cancerres.aacrjournals.org on September 27, 2021. © 2017 American Association for Cancer Research. Author Manuscript Published OnlineFirst on February 24, 2017; DOI: 10.1158/0008-5472.CAN-16-2479 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Abstract

Genomic profiling is widely predicted to become a standard of care in clinical

oncology, but more effective data sharing to accelerate progress in precision medicine

will be required. Here we describe cancer-associated genomic profiles from 18,004

unique adult cancers. The dataset was composed of 162 tumor subtypes including

multiple rare and uncommon tumors. Comparison of alteration frequencies to The

Cancer Genome Atlas (TCGA) identified some differences and suggested an enrichment

of treatment-refractory samples in breast and lung cancer cohorts. To illustrate novelty

within the dataset, we surveyed the genomic landscape of rare diseases and identified

an increased frequency of NOTCH1 alterations in adenoid cystic carcinomas compared

to previous studies. Analysis of tumor suppressor gene patterns revealed disease

specificity for certain genes but broad inactivation of others. We identified multiple

potentially druggable, novel and known kinase fusions in diseases beyond those in

which they are currently recognized. Analysis of variants of unknown significance

identified an enrichment of SMAD4 alterations in colon cancer and other rare

alterations predicted to have functional impact. Analysis of established, clinically

relevant alterations highlighted the spectrum of molecular changes for which testing is

currently recommended, as well as opportunities for expansion of indications for use of

approved targeted therapies. Overall, this dataset presents a new resource with which

to investigate rare alterations and diseases, validate clinical relevance, and identify

novel therapeutic targets.

Introduction

Large-scale genomic datasets, such as The Cancer Genome Atlas (TCGA), have

increased our understanding of the molecular mechanisms driving tumorigenesis (1–3).

Public availability of this data has facilitated novel discoveries, validated rare findings,

and allowed for the incorporation of genomic features into clinical trial design (4). As

more cancer patients undergo clinical genomic profiling, sharing these data with the

broader research community is critical for accelerating precision medicine (4).

The value of genomic data is apparent in multiple cancers where molecular

alterations define distinct clinical groups. For example, glioblastomas can be divided into

four molecular subtypes, each with distinct survival and response rates to standard

therapies (5,6). Insights from cancer genomics analyses have also led to the

development and validation of targeted treatment options against molecularly-matched

alterations that can be more effective and less toxic than traditional chemotherapeutic

regimens. For example, in non-small cell lung cancer (NSCLC), alterations in 8 genes are

associated with sensitivity to targeted inhibitors and genomic analyses of these targets

is now recommended in treatment guidelines (7). Additionally, broad genomic features,

such as total tumor mutational burden (TMB), have been proposed as potential

biomarkers of sensitivity for immune checkpoints inhibitors (8). Genomic information

can also inform clinical trial design to better identify patients likely to respond to

targeted inhibitors. Multi-arm umbrella trials such as NCI-MATCH (NCT02465060) and

LUNG-MAP (NCT02154490) are using genomic features to select appropriate treatment

arms. Basket trials selecting patients based on molecular alterations versus tumor type

have also been developed for alteration-specific inhibitors (9). Across cancers, patients

enrolled in molecularly matched clinical trials have demonstrated superior survival

versus random trial enrollment (10).

The sharing of genomic data is critical to further our understanding of molecular

drivers and to develop effective therapies. Here, we present genomic profiles from

18,004 unique adult solid tumors that underwent targeted genomic profiling as part of

routine clinical care. This collection represents a vast diversity of tumor subtypes,

including many rare diseases not profiled as part of large-scale efforts. High-level

analysis identified novel alterations in common diseases and confirmed the prevalence

of alterations that were underrepresented previously in small cohort studies.

Additionally, we highlight the spectrum of clinically relevant alterations with established

roles in determining drug sensitivity. By making this data available to the broader

research community, it is anticipated that this information will serve as a source of

discovery and validation for projects aimed at improving cancer treatments and

outcomes.

Materials & Methods

Sample profiling

This study was reviewed and approved by the Western Institutional Review

Board (WIRB). Samples were submitted between 2012-2014 to a CLIA-certified, New

York State-accredited, and CAP-accredited laboratory (Foundation Medicine) for hybrid

capture followed by next-generation sequencing (NGS). The pathologic diagnosis of each

case was reviewed and tumor content determined from hematoxylin and eosin (H&E)

stained slides. All samples that advanced to DNA extraction contained a minimum of

20% tumor cells. The majority of samples contain <50% tumor content (Supplementary

Figure S1A). DNA was extracted from formalin fixed paraffin embedded (FFPE) 10

micron sections. Adaptor-ligated DNA underwent hybrid capture for all coding exons of

287 cancer-related genes plus select introns from 19 genes frequently rearranged in

cancer (Supplementary Table S2). Captured libraries were sequenced to a median exon

coverage depth of >600x using Illumina HiSeq sequencing technology (Supplementary

Figure S1B). To protect against inadvertent re-identification, samples in ultra-rare

disease types (<5 samples) were removed. In cases where multiple samples from the

same individual were tested (determined via a SNP-genotype based approach), only one

sample was included. No further selection was performed and all samples meeting

these criteria are included in this dataset.

Sequencing analysis and deposition of data

Resultant sequences were analyzed for short variants (base substitutions,

insertions/deletions (indels)), copy number (CN) alterations (focal amplifications and

homozygous deletions), and select gene fusions using the hg19 reference genome, as

previously described (11). Since tumor samples were sequenced without a

corresponding matching normal sample, additional custom filtering was applied to

highlight cancer-relevant alterations and reduce noise from benign germline events

(Supplementary Figure S2; Supplementary Methods). Sequence analysis results are

available publicly in the Genomic Data Commons (GDC) portal (https://gdc.nci.nih.gov/).

Analysis of TCGA data

TCGA data (2016-01-28), analyzed via Firehose, was accessed via the

‘firehose_get’ tool. See the Supplementary Methods for additional details. Survival

analysis was determined via log rank test.

Mutation Hotspot Caller

We performed a hotspot analysis of missense and nonsense mutations to

prediction hotspot changes within a given gene. See Supplementary Methods for

additional details.

Results

Overview of genomic and clinical characteristics

The Foundation Medicine (FM) adult dataset consists of 18,004 unique tumor

samples that underwent genomic profiling as part of standard clinical care. Each sample

was assigned a detailed diagnosis; these detailed subtypes were then grouped into

broader disease categories. In total, 16 broad disease categories were created with

tumors from 162 unique disease subtypes (Figure 1, A&B; Supplementary Figures S3-

S11). The most common disease categories are thoracic cancers (20.7%, 12 subtypes), GI

cancers (17.1%, 15 subtypes), breast cancers (14.3%, 8 subtypes), gynecologic cancers

(8.3%, 25 subtypes), and hepato-pancreato-biliary cancers (7.1%, 15 subtypes). The

remaining 32.5% of samples include genitourinary, CNS, neuroendocrine, head and

neck, melanoma, and additional less common cancer types. Samples for which detailed

pathologic information was unavailable were grouped into general “not otherwise

specified” (nos) categories within each disease category. Among all the specific and

“nos” categories, the most common disease subtypes were lung adenocarcinoma

(13.0%), colon adenocarcinoma (9.6%), breast carcinoma (nos) (6.6%), breast invasive

ductal carcinoma (6.5%), and lung non-small cell carcinoma (nos) (3.4%). Of the 162

detailed diagnostic subtypes, 63% were comprised of 50 or fewer samples, including

multiple rare diseases (Supplementary Table S2). All detailed subtypes had at least 5

unique tumors. In addition, 13.0% of the diseases subtypes contained 200 samples or

greater (range 203-2345) allowing for robust statistical analyses in common diseases.

Genes frequently altered across the dataset included the TP53 (54%), KRAS (21%),

CDKN2A (19%), PIK3CA (14%), and CDKN2B (12%) (Figure 1C).

Gender information was available from all but 9 samples and showed a slight

bias towards females (56.7%) versus males (43.3%) (Figure 1D). This bias can be

explained in part by the large number of breast and GYN cancer samples within the

dataset (Figure 1A). The average age of patients at the time of genomic profiling was

57.7 years (median: 59 years, range: 19-88 years) (Figure 1D). Patients 89 years old or

older at the time of testing were excluded to comply with privacy guidelines.

Information about disease recurrence and previous treatment histories was not

available for the cohort.

Comparison of alteration frequencies to known datasets

In contrast to other large-scale genomic profiling efforts that employed multiple

technologies (i.e. DNA-seq, CN arrays, and RNA-seq) to analyze genomic alterations,

samples within this cohort were analyzed for all classes of genomic alterations on a

single uniform platform. Due to the clinical setting in which these samples were profiled,

no selection criteria (e.g. fresh versus archival tissue, primary versus metastatic tissues,

or pre- versus post-treatment samples) was applied. Therefore, we hypothesized that

the spectrum and frequency of genomic alterations would vary compared to those from

other large-scale profiling efforts (i.e. TCGA) that utilized multiple technologies and

applied stringent selection criteria. We analyzed these differences using TCGA samples

for which both CN (GISTIC2) and mutation data (MutSig2CV) existed and for which

disease subtypes could be mapped readily between the two datasets. Finally, we

included only diseases for which at least 200 samples existed in both cohorts to avoid

sampling bias. (Figure 2; Supplementary Table S3).

We first investigated the impact of methodological differences. Differences in CN

alteration frequencies were observed across many tumors (Figure 2, green bars). These

discrepancies may be explained in part by different technologies used to measure these

events by TCGA (SNP arrays) and FM (NGS-based modeling), differences in sample input

requirements (i.e. >70% tumor content versus >20% tumor content), annotation

thresholds (FM requires focal segments for most genes and >6-8 copies depending on

tumor ploidy), or functional status (plotted FM data excludes variants of unknown

significance, VUSes). However, CN differences appeared to be tumor type specific as few

discrepancies were observed in breast and bladder cancer cohorts whereas multiple

differences were observed in ovarian cancers, head and neck squamous cell carcinomas,

and lung adenocarcinomas. These CN differences were not due to the high proportion of

metastatic samples in the FM dataset since restricting the FM dataset to local disease

produced similar results (Supplementary Figure 12-13). Although the low cellularity

typical of FM samples could potentially explain the lower rate of CN detection, the high

sequencing depth applied allows for high CN sensitivity >85% even at 20-30% cellularity

(12). Thus, the differences in copy number observe are most likely due to differences in

technology, annotation, and/or sample differences (i.e. differences in proportion of

tumor subtypes).

We next investigated differences in mutation frequencies. The largest mutation

frequency difference between the two datasets was observed for LRP1B in lung

adenocarcinomas (6.5%, versus 40.9%). Large discrepancies for LRP1B are also seen in

melanomas and head and neck squamous cell carcinomas. These disparities are largely

explained by the many VUSes in this gene (excluded from comparison in Figure 2) that

result from the high background mutation rate in these tumor types (Supplementary

Figure 14). In contrast, the higher TP53 alteration frequency in breast and

uterine/endometrial cancers could not be accounted for by filtered VUSes or differences

in the types of alterations reported, as rates of missense, nonsense, splice, and

frameshift mutations were similar between the two cohorts (Supplementary Figure

S15A). Similarly, tissues sites suspected to yield lower quality DNA (i.e. pleural fluid) did

not show evidence of distinct mutation detection (Supplementary Figure S15B).

Further, within the FM dataset, TP53 alterations are enriched in local disease indicating

the discrepancy is not due to the high proportion of metastatic samples (see

Supplementary Table 4 for genes altered at different rates in local versus metastatic

disease). Therefore, differences in mutation frequencies may represent sample

differences between the datasets.

To explore biological differences between the dataset, we investigated specific

molecular trends associated with advanced refractory tumors. For example, EGFR short

variants (point mutations and indels) were slightly more frequent within FM lung

adenocarcinomas versus TCGA (20.3% versus 14.5%, p=0.003). However, the EGFR

T790M mutation, associated with acquired resistance to targeted inhibitors, was

observed at a much higher frequency within FM samples compared to the treatment-

naïve TCGA dataset (4.1% versus 1.2%, p=5.3x10-7). A similar trend was also observed in

breast cancers, where the higher frequency of ESR1 alterations in the FM dataset (9.5%

versus 3.7%, p=2.8 x 10-9) suggested a selection for samples with acquired resistance to

endocrine therapies. These data suggest an enrichment of treatment refractory breast

and lung cancer samples within the FM collection.

Collectively, these comparisons confirm differences between TCGA and FM.

Methodological differences between the datasets are likely contributing a minor role to

the observed discrepancies. However, the enrichment of resistance-associated

alterations in breast and lung cancers suggests a biological difference between the

tumors within our cohort and TCGA. Although molecular signatures of therapeutic

resistance are less well understood in diseases not treated routinely with targeted

therapies, one can infer that these tumors are likely from advanced stage cancers.

Novel variants and disease enrichment patterns

We next applied an internally developed hotspot-calling algorithm (see

Supplementary Methods) and in silico prediction tools (MutationAssessor and

PolyPhen-2) (12,13) to search for novel functional variants of unknown significance

(VUS). Using the hotspot caller, we identified several mutation hotspots in SMAD4, a

tumor suppressor gene previously implicated in colorectal cancers (14). The hotspots

identified were mostly nonsense mutations, consistent with a tumor suppressor role

(Figure 3A). However, we did observe several hotspot missense mutations including

known hotspots such as D351A/G/V/Y, R361C/H/S, P356H/L/R/S, D537A/E/G/H/V/Y

(15,16), and novel hotspots such as A118V, E330K/*, G419M/R/W, and D493H/N,

W524C/G/R (Figure 3A). We also observed hotspot mutations in the related pathway

components SMAD2 (T303 and S464) and TGFBR2 (R495 and R528) (Figure 3B,C). Since

the TGFβ/SMAD pathway is a tumor suppressor pathway, we analyzed hotspot

mutations, truncations, and homozygous deletions (collectively identifying TGFβ/SMAD

pathway altered samples). Alterations in this pathway were most frequent in GI and

Pancreato-Hepato-Biliary cancers with 15.8% (487/3076) and 13.6% (175/1284) of total

cases, respectively (Figure 3D). Alterations in this pathway were non-overlapping

although significance could not be addressed due to the small sample size (Figure 3E).

We did observe a significant co-occurrence with KRAS alterations (OR = 1.70, p = 9.6 x

10-6) and a significant mutual exclusivity with TP53 alterations (OR = 0.62, p=2.34 x 10-4)

(Figure 3F). To demonstrate clinical relevance, we incorporated survival data from TCGA

samples harboring TGFβ/SMAD pathway alterations (14). Colon adenocarcinomas were

separated into TGFβ/SMAD pathway altered (n = 75; hotspot

mutation/truncation/homozygous deletion) versus TGFβ/SMAD pathway unaltered (n =

254) groups. The TGFβ/SMAD-altered group exhibited reduced progression free survival

that trended towards significance (p = 0.06; log rank test) (Figure 3G).

To capture rare alterations, we also analyzed the functional impact of VUS point

mutations occurring in at least 5 samples using MutationAssessor and PolyPhen-2 tools

(12,13). A merge of these outputs identified 23 unique point mutations in 11 genes with

predicted functional impact across both algorithms (Supplementary Table S5). All

mutations also reached significance using the hotspot caller described above. In

addition to the mutations described above, this VUS analysis identified multiple

alterations in ERBB3 (T355I and T389I), BRIP1 (R762C and R251C), KEAP1 (G523V,

R413C, and G419W), and SMARCA4 (E882K, P913L, R973W, R1135Q, and R1192H) and

intriguing variants in PTEN (D24H), FLT1 (E432K), STK11 (P179R), LRP1B (G401E), ESR1

(A361V), and CDKN2A (G101V) predicted to have functional impact. Interestingly, these

variants tended to occur in tumor types associated with alterations in that gene. For

example, 3/6 FLT1 E432K alterations were in melanomas, 21/32 of the various SMAD4

alterations were found GI and hepato-billiary cancers, and 9/15 of the KEAP1 alterations

were found in thoracic cancers. The non-random distribution of these VUSes strongly

implicates them as functional alterations. Of note, since matched germline DNA was not

tested, we cannot completely discount the possibility that some of these alterations

may be rare germline alterations. However, somatic/germline status was predicted with

a novel, internally developed algorithm that assesses germline status based on allele

frequency and tumor purity/ploidy (Sun et al., in review 2016). For example, in a sample

with moderate cellularity (~50%), in copy neutral, diploid regions, somatic alteration

allele frequencies will be impacted by cellularity and have allele frequencies ~25%. In

contrast, heterozygous germline variants will not be impacted by cellularity and will

have allele frequencies ~50%. These estimates suggest that >90% of the VUSes

predicted to have a functional impact are somatic events (data not shown).

Genomic analysis of rare diseases uncovers higher frequencies of NOTCH1 and BCOR

alterations in adenoid cystic carcinomas

We investigated the genomics of rare tumors given that many of the subtypes

within the FM dataset were excluded from large-scale analyses (i.e. TCGA) or profiled

only as part of small cohorts. Interesting results were observed in adenoid cystic

carcinomas (ACCs) of the head and neck region (n=156 total), including head and neck

ACCs (n=78), salivary gland ACCs (n=57), tracheal ACCs (n=7), and unknown primary

ACCs (n=14). In agreement with recent findings of 36 recurrent and metastatic ACCs

(17), the most frequent alterations occurred in NOTCH1 (23%) (Figure 4A). NOTCH1

alterations were clustered in the C-terminal PEST domain of the protein (Figure 4B), and

were significantly enriched within this disease (Supplementary Table S6). NOTCH1 PEST

domain alterations are weakly activating by themselves (18). However, they are

synergistic with HD domain mutations in cis and can significantly increase the activity of

this protein when this combination of alterations is present. Interestingly, 7 samples

harbored mutations in both the PEST and HD domains, suggesting a potential

mechanism through which activity of this gene could be altered (Figure 4B).

Unfortunately, phasing of the mutations was impossible to determine definitively due to

the distance between mutations and the length of the sequencing reads. However, two

tumors contained a third alteration (E794* and N390fs*243) towards the 5’ end of

NOTCH1, likely disrupting one allele and suggestive that the other two alterations are in

cis. Collectively, other genomic studies have investigated 111 total ACC tumors,

including 28 samples also represented within this dataset, and reported NOTCH1

missense and nonsense alterations in 5-10% of samples (19–21). This analysis confirms

findings from smaller studies that NOTCH1 is the most commonly altered gene in ACCs

at ~24% and extends it by providing multiple examples of co-occurring PEST and HD

domain alterations. Further work to evaluate the effect of these alterations is warranted

as multiple inhibitors of this protein are currently in clinical trials (22).

The second most common alterations in ACCs occurred in the tumor suppressor

gene BCOR (17%) (Figure 4A). All variants are predicted to inactivate this protein (Figure

4C). These results agree with previous findings where 4/36 ACCs were found to have

truncating mutations in BCOR. Together, these results establish BCOR inactivation as a

signature event in ACC. BCOR alterations have been described in myelodysplastic

syndromes where they are associated with poor prognosis and shorter overall survival

(23). They have also been described in multiple pediatric tumors where they are thought

to play a role in chromatin modification (24). Both BCOR and NOTCH1 alterations co-

occurred with other events (Supplementary Figure S16).

Identification of novel fusion events

We investigated the spectrum of fusions involving 8 clinically relevant kinases

(ALK, BRAF, EGFR, NTRK1, PDGFRA, RAF1, RET, and ROS1) with established druggability.

In total, we identified 19 novel fusions with structures similar to known oncogenic

fusion proteins and multiple known fusions in diseases different from those in which

they were reported originally.

Nine novel fusions involved the serine threonine kinases BRAF or RAF1 (Figure

5). These included fusions with four novel fusions partners in diseases known to be

driven by these events, such as astrocytoma, melanoma, and prostatic acinar cell

carcinoma (Figure 5A). RAF1 fusions were also observed in 5 other disease types (Figure

5B). Interestingly, the PRKAR2A-RAF1 fusion was recurrent and observed in both a lung

adenocarcinoma and an unknown primary melanoma. In addition, we also observed 7

novel tyrosine kinase fusions involving ALK, RET, ROS1, and NTRK1 (Figure 5C) in non-

small cell lung cancers and thyroid cancer. Novel fusions involving ALK, RET, ROS1, and

NTRK1 were also observed in a colon adenocarcinoma, a GI neuroendocrine tumor, and

a uterine endometrial carcinoma (Figure 5D).

Although kinase fusions can be hallmarks of certain cancers, we observed known

fusions outside of the diseases in which they were identified originally. CLTC-ALK and

STRN-ALK, have been observed in aggressive thyroid cancers and soft tissue

malignancies (25,26). Within the FM cohort, these fusions were observed in a lung

adenocarcinoma and an epithelioid peritoneal mesothelioma, respectively

(Supplementary Figure S17). Diagnosis of the epithelioid peritoneal mesothelioma is

supported by IHC staining (positive for cytokeratin 7, calretinin and vimentin; negative

for CEA, B72.3, and TTF1). While a subset of lung adenocarcinomas is known to be

driven by ALK fusions, CLTC-ALK has yet to be described in this disease. In contrast,

kinase fusions have not been noted in mesothelioma; STRN-ALK represents a novel, yet

rare (1/184, 0.5%), driver event in this disease. RET fusions in thyroid and lung cancers

are well characterized (27,28). Here, two breast cancers were found to harbor the

oncogenic CCDC6-RET fusion (Supplementary Figure S17). Two similar onocgenic RET

fusions (NCOA4-RET and KIF5B-RET) were also identified in a rare liver

cholangiocarcinoma and an ovarian epithelial carcinoma, respectively (Supplementary

Figure S17). BRAF fusions have been described in melanoma, thyroid cancers, and

pediatric brain cancers (29). A single thyroid papillary carcinoma was found with a

MAD1L1-BRAF fusion identified previously in melanoma (30). We also observed the

known TMEM106B-ROS1 fusion (31) in a liver cholangiocarcinoma. Interestingly,

GOPC(FIG)-ROS1 has been reported in a glioblastoma cell line (32), in a lung

adenocarcinoma(33), and in rare biliary tract carcinomas (34). Here, we observed this

fusion in a small intestine adenocarcinoma and confirm its presence in a human

glioblastoma sample (Supplementary Figure S17). Finally, we observed an imatinib

sensitive hypereosinophilic syndrome fusion, FIP1L1-PDGFRA (35), in a glioblastoma

(Supplementary Figure S17).

Spectrum of known clinically relevant alterations across diseases

Recent publications have suggested a broad spectrum of genomic changes in

clinically relevant targets (29,36–38), and expanded analyses are warranted to identify

more patients predicted to be sensitive or resistant to targeted therapies. Furthermore,

the identification of drug sensitivity and resistance biomarkers across multiple

indications suggests that targeted agents may have broader utility beyond that for

which they were approved originally. We surveyed the spectrum of known clinically

relevant genomic changes to identify 1) the spectrum of these changes in indications for

which testing is currently recommended and 2) potential opportunities for broader

utility of approved targeted agents.

In colorectal cancers (CRCs), activating mutations in KRAS are predictive of poor

response to cetuximab. Alterations in hotspot exons 12 and 13 were observed in

908/1986 (45.7%) CRCs. Recently, expanded guidelines adopted by ASCO and others

recommend testing for alterations in exon 2 (codons 12 and 13), exon 3 (codons 59 and

61), and exon 4 (codons 117 and 146) in both KRAS and NRAS (39). These extended RAS

testing guidelines captured an additional 188 CRC samples (9.5%) within our dataset.

Beyond these extended guidelines, we also observed activating KRAS and NRAS

amplifications and mutations at codons 14 and 22 in an additional 29 CRC samples

(1.5%) (40,41) (Figure 6A). These data provide robust estimates of RAS alterations in

CRCs, utilizing both ASCO guidelines and the current state of knowledge, which is critical

when considering EGFR targeted therapies.

Since mutations in the MAPK pathway are known drivers of multiple

gastrointestinal cancers, we performed a survey of KRAS, NRAS, HRAS, and BRAF

alterations across cancers of the gastrointestinal and hepato-biliary tracts (Figure 6B).

Gastroesophageal junction adenocarcinoma was unique in that it had a high proportion

of KRAS amplifications without a KRAS mutation (16.3%). Appendix adenocarcinomas

had a similar rate of MAPK alterations (64.5%) as neighboring small intestine (56.6%)

and CRCs (60.3%). MAPK alterations in cancers of the biliary tree (see Figure 6B)

included a high frequency in bile duct adenocarcinomas (46.3%) and pancreatic cancers

(89%) and a low frequency in gallbladder adenocarcinomas (11.5%) and liver

cholangiocarcinomas (29.8%).

We next investigated the spectrum of clinically relevant insertions and deletions

(indels) in EGFR (EGFRvIII rearrangements were also identified and are discussed in a

subsequent section). The most prevalent EGFR exon 19 indel was the canonical E746-

A750 deletion, although the length of deletions at amino acids 746 and 747 ranged from

3-7 amino acids (Figure 6C). We also observed rare deletions of 8 amino acids at

positions 751 and 752. In contrast, drug-resistant EGFR exon 20 insertions lacked a

single dominant location and length, but the vast majority occurred between amino

acids 769-774 and inserted 1-3 residues within this region (Figure 6D). Analogous ERBB2

insertions primarily consisted of a 4 amino acid duplication between residues 772-775,

however, these insertions ranged from 1-4 amino acids between residues 772 and 780

(Figure 6E). The diversity of these mutations has implications for robust diagnostic

detection and developing drugs to target this heterogeneous set of insertions (37).

Current data suggest that ERBB2 amplifications, oncogenic point mutations, and

activating insertions can confer clinical sensitivity to ERBB2 targeted agents (42). We

observed recurrent activating ERBB2 alterations across 15 tumor types (Figure 7A),

including new trends such as ERBB2 amplifications in cervical cancer and skin squamous

cell carcinomas. ERBB2 point mutations have been described in cervical cancers (43),

but amplifications suggest an alternate mechanism through which the gene can be

activated in this disease. Consistent with previous studies, ERBB2 activating mutations in

cervical cancers did not co-occur with copy number alterations (Supplementary Figure

S18). To our knowledge, ERBB2 amplifications in skin squamous cell carcinoma

represent a novel therapeutic target in this disease.

Amplification of the CDK4/6 locus has been associated with response to the

CDK4 inhibitor palbociclib in breast cancers and liposarcomas (44,45). Within this

cohort, we observed CDK4/6 amplification across 43 tumor types (Figure 7B). Novel

findings included amplification of these genes in gallbladder carcinomas (11.5%) and

oligodendrogliomas (6.6%). Preclinical models have suggested that this event may

contribute to oligodendroglioma formation (46).

MET amplification was observed in glioblastoma samples at an appreciable

frequency (2.1%) (Figure 7C). We explored whether this finding correlated with

overexpression of MET mRNA by incorporating expression data from TCGA glioblastoma

samples. Although few TCGA GBM cases exist with MET amplification, amplification was

associated with increased expression (Supplementary Figure S19). Interestingly, at least

one case report has demonstrated clinical sensitivity to the MET inhibitor, crizotinib, in

this disease (47). A similar observation was confirmed in AKT1 E17K-mutant colorectal

adenocarcinomas (Figure 7D). While this mutation has been observed previously in this

disease, frequencies varied from 0%-8.2% (14,48,49). Our data confirm the rare

occurrence of this alteration in ~1.0% of routine CRC samples. Interestingly, in contrast

to a previous study (50), we observe AKT1 E17K mutant colorectal samples to be

enriched for KRAS alterations (p=0.02) but not BRAF alterations (Supplementary Figure

S20).

In addition to potentially targetable alterations that occur across diseases, we

also observed striking patterns of disease specificity for certain alterations. EGFRvIII

rearrangements and extracellular activating mutations were found almost exclusively in

glioblastomas while activating indels in EGFR occurred almost exclusively in NSCLCs.

Although we also saw an appreciable rate of EGFR indels in unknown primary

adenocarcinomas, it is likely that these represent NSCLC samples for which incomplete

pathology information was available (J. Ross, personal communication). A similar trend

was also observed for ROS1 rearrangements. These events were observed primarily in

NSCLC and a small proportion of glioblastomas. Both of these rates are consistent with

published reports, and suggest that ROS1 fusions show tissue specificity.

While many oncogenic alterations cluster within diseases and are targetable

directly, some inhibitors rely on the status of tumor suppressor genes (TSGs) as

biomarkers of response. For example, deleterious alterations in BRCA1/2 are associated

with sensitivity to PARP inhibitors (51), and multiple trials require intact p53 (TP53) as

enrollment criteria (NCT01760525, NCT02143635, NCT02264613). Therefore, we

investigated patterns of TSG alterations. Unsupervised clustering of alterations within a

curated list of TSGs (Supplementary Table S7) identified unique patterns of inactivation

across solid tumors (Supplementary Figure S21). Genes including TP53 and CDKN2A/B

were altered uniformly across multiple solid tumors. In contrast, other TSGs displayed

disease-specific clustering, such as APC alterations within GI cancers. Multiple novel

disease-gene associations were also present. For example, alterations in BCOR, NOTCH1,

KDM6A, CREBBP, and KMT2D clustered primarily in ACCs. Collectively, these data

suggest patterns of TSG inactivation that may be disease specific, similar to patterns for

some oncogenes. Further research to understand this tissue selectivity is warranted.

Discussion

We describe herein a dataset of 18,004 unique adult solid tumors that

underwent genomic profiling as part of routine clinical care. This collection represents

“real world” specimens that were not selected for any features prior to sequencing. The

dataset was composed of 162 disease subtypes, including many rare and unusual

tumors not included previously as part of larger sequencing efforts. Additionally,

common tumors (e.g. breast, lung, and colon) are represented by thousands of samples,

enabling robust statistical analyses as well as validation of rare variants. Comparison of

alteration frequencies to TCGA, where possible, identified some significant differences,

mostly in CN frequencies. Detailed examination suggests that both technical (i.e.

platform, annotation) and sample differences underlie between the discrepancies

between the two datasets.

We also observed an enrichment of treatment refractory samples in FM breast

and lung cancer cohorts based on an increased frequency of alterations associated with

acquired resistance to targeted therapies in these diseases. To exemplify novelty within

the FM dataset, we surveyed the genomic landscape of rare diseases and identified

NOTCH1 alterations in ACCs at a higher frequency compared to previous studies. We

also identified multiple potentially druggable novel kinase fusions as well as known

fusions in diseases beyond those in which they are currently recognized. Analysis of

VUSes identified a clinically significant enrichment of SMAD4 alterations in colon cancer,

as well as multiple other rare alterations predicted to have functional impact. A survey

of clinically relevant alterations highlighted the spectrum of molecular changes for

which testing is recommended as well as opportunities for expansion of approved

targeted therapies. Clustering of alterations in TSGs revealed patterns of disease

specificity for certain genes, but broad inactivation of others. This dataset is rich with

discovery potential and presents a new resource in which to investigate rare alterations

and diseases, validate clinical relevance, and identify novel therapeutic targets.

To our knowledge, this dataset represents the largest collection of tumors to

date profiled on a single uniform platform. The high unique sequencing coverage

(>600x) across all targets enables accurate detection of all classes of genomic variants,

even in impure clinical specimens. Previous validation has optimized this assay for

sensitive and specific detection for all classes of variants down to low mutant allele

frequencies (11). The samples within this dataset lack sequencing of patient-matched

normal tissue, but multiple steps have been taken to enrich for significant cancer-

associated variants (see Supplementary Methods). These include inclusion of 1) all

truncation events in tumor suppressor genes, 2) known pathogenic germline events,

and 3) uncharacterized alterations reported previously in cancer (Supplementary Figure

S2). To minimize the number of benign germline variants, those variants not meeting

the criteria above were filtered through online databases (ExAc and 1000 Genomes) to

remove events recognized currently as benign polymorphisms. Collectively, the

uniformity of the data and the stringent filtering to enrich for cancer-associated

alterations facilitate comparisons and enhance the discovery potential for variants

contributing to tumorigenesis.

The dataset can be used by basic researchers to identify novel findings for

validation and to validate previous observations, especially those involving rare diseases

and rare variants. Our preliminary analyses exemplify how hypothesis-generating

discoveries within this cohort can be integrated with existing data. For example, the

identification of novel SMAD4 genomic alterations in colorectal adenocarcinomas was

expanded using the TCGA cohort to investigate survival differences among patients.

Additionally, MET amplifications in glioblastoma were shown to correlate with mRNA

overexpression of this target in TCGA, a finding that may have been unappreciated in

the past due to small datasets and the rarity of the event. A data collection of this size

also allows for pan-cancer analyses to better understand tissue-specific patterns of

alterations, such as TSG inactivation. These findings can be used to plan thoughtful

functional follow-up experiments. This resource also has applicability to clinical

oncology and drug development. We highlight the spectrum of clinically relevant

molecular markers and show that a wide variety of alterations exist in targets for which

established routine clinical tests exist. This resource can also be used to explore

opportunities for drug expansion with new or approved targeted agents for which

biomarkers of therapeutic sensitivity or resistance are known. For example, we highlight

multiple alterations, including MET amplification, ERBB2 amplification and activating

point mutations, and amplification of CDK4/6, that occur across multiple diseases. In

contrast, we observed that ALK rearrangements and EGFR activating alterations are

confined to specific diseases and very rarely observed outside of those tissues.

The lack of clinical data is a limitation of this dataset. Information about

exposure to previous treatments, survival, and response rates was unavailable. Since

these samples were profiled on a clinical platform, and not as part of a research study,

genomic information is only available for those 295 genomic targets deemed to have

clinical relevance today. While the role of genomic changes in cancer development and

treatment response is well studied, it is likely that other changes in methylation,

expression, and non-coding DNA regions may have implications and would not be

captured within this dataset.

The National Cancer Moonshot Initiative has emphasized that data sharing is

essential to accelerate progress in oncology. Academic, private, and public sectors have

an obligation to patients, researchers, and clinicians to share data, knowledge, and

insight across the field. Large-scale sequencing projects have profiled many common

tumors but often lack robust sample numbers for rare diseases and variants. The public

availability of large genomic datasets, such as the one described herein, enables the

broad use of this data across multiple disciplines, and is designed to remove barriers to

progress. The insights gleaned from this data release will be instrumental in accelerating

research and development efforts for targeted agents and immunotherapies.

Acknowledgements

We thank Susan Hager, Allen Nunnally, and Shannon Roberts (Foundation

Medicine) for their contributions to this project.

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Figure Legends

Figure 1: Clinical and genomic characteristics of samples within the dataset.

(A.) Samples were grouped into 16 broad disease categories describing their

tissue of origin. Not shown on the graph (grouped into the other category for

visualization) are 4 additional diseases (Unclassified 0.9%, Non-Melanoma Skin Cancers

0.8%, Mesenchymal Cancers 0.6%, and Germ Cell Tumors 0.2%). (B.) Additionally, each

sample was assigned a detailed label that represented its pathologic diagnosis. Subtype

distributions for the top three broad categories (thoracic, GI, and breast cancers) are

depicted in the smaller surrounding charts. Thoracic cancers included samples from 12

disease subtypes (the top 5 are depicted in the small chart). GI cancers included 15

disease subtypes (the top 10 are depicted in the small chart). Breast cancers were

comprised of samples from 8 subtypes (the top 4 are depicted in the small chart). See

Figures S3-11 and Supplementary Table S2 for further details about subtypes. (C.) Long

tail distribution of alterations across the entire dataset. SV-short variants (includes

missense mutations and indels), CN-copy number alterations, RE-rearrangements, Mult-

multiple events. (D.) Clinical characteristics of samples within the dataset including age

and gender distributions.

Figure 2: Comparison of alteration frequencies between TCGA and FM datasets.

Frequencies of alterations in analogous tumor subtypes were compared

between FM and TCGA datasets. FM data excludes VUSes. *Neighboring gene was used

to determine copy number status at this locus. **Significant differences in alteration

frequencies (p<0.05). BRCA-breast invasive carcinoma, BLCA-bladder urothelial

carcinoma, OVCA-ovarian carcinoma, UCEC-uterine corpus endometrial carcinoma,

GBM-glioblastoma, COAD-colon adenocarcinoma, SKCM-skin cutaneous melanoma,

STAD- stomach adenocarcinoma, LUAD-lung adenocarcinoma, and HNSC-head and neck

squamous cell carcinoma.

Figure 3: Novel variants in SMAD4 and SMAD signaling pathway components.

(A.) Hotspot alterations within SMAD4 identified by a hotspot analysis. These

include both known hotspots (D351A/G/V/Y, R361C/H/S, P356H/L/R/S,

D537A/E/G/H/V/Y), and novel hotspots (A118V, E330K/*, G419M/R/W, and D493H/N,

W524C/G/R). (B. – C.) Alterations within SMAD pathway genes SMAD2 and TGFRB2

were also analyzed. (D. – F.) Analysis of all hotspot and loss of function (truncation and

homozygous deletions) mutations in these three genes (D.) for incidence across disease

groupings and (E.) co-occurrence in colorectal adenocarcinoma. (F.) Co-occurrence of

TGFβ/SMAD pathway alterations in colorectal adenocarcinoma with KRAS alterations

and significant mutual exclusivity with TP53 alterations is shown. (G.) TCGA Colon

adenocarcinomas were separated into TGFβ/SMAD pathway altered (n = 75 hotspot

mutations, truncating mutations, and homozygous deletions) versus TGFβ/SMAD

pathway unaltered (n = 254) groups. The TGFβ/SMAD mutated group exhibited reduced

progression free survival that trended towards significance (p = 0.06; log-rank test).

Figure 4: NOTCH1 and BCOR variants in adenoid cystic carcinomas.

(A.) A long tail plot of alterations within adenoid cystic carcinomas (ACCs) shows

a high prevalence of alterations in NOTCH1 and BCOR. (B.) The distribution of alterations

within NOTCH1 reveals a clustering of inactivating mutations within the C-terminal PEST

domain. Arcs represent mutation pairs co-occurring in the same tumor. Mutations in

tumors with 3 NOTCH1 mutations (n=2) are highlighted with arrowheads color coded by

sample. Protein domains are: EGF-like (green), calcium-binding EGF (red), hEGF-like

(blue), LNR (yellow), NOD (purple), NODP (orange), Ank (fuchsia, dark red, cyan), and

DUF (mustard). NOD and NODP together represent the homopolymerization domain

(HD) and DUF is contained within the PEST domain. (C.) The distribution of alterations in

BCOR reveals inactivating events across the gene. Diagrams were generated using

MutationMapper (see Methods).

Figure 5: Novel fusions in known and unknown diseases.

(A.) Five novel BRAF and RAF1 (CRAF) fusions were identified in diseases where

rearrangements involving these genes are known to be oncogenic. (B.) Additionally, we

also observed five novel RAF fusions in other diseases. (C.) Novel fusions involving ALK,

RET, ROS1, and NTRK1 were identified in non-small cell lung carcinomas (NSCLCs) and a

papillary thyroid carcinoma. Kinase fusions within these diseases are known to play a

tumorigenic role. (D.) Three novel tyrosine kinase fusions were also identified in other

diseases.

Figure 6: Diversity of clinically relevant alterations across the dataset

(A.) Distribution of clinically relevant KRAS alterations in colorectal

adenocarcinomas. (B.) Distribution pattern of all RAS alterations in GI cancers. (C-E.)

Indel alterations in EGFR (C. and D.) and ERBB2/HER2 (E.) can vary in length. Numbers

on the left side of the graphs correspond to the codon positions; heatmaps display the

number of samples.

Figure 7: Spectrum of druggable alterations across cancers

Distribution of alterations across the top 15 disease types for ERBB2/HER2 (A.),

amplifications in CDK4/6 (B.), amplifications in MET (C.), and activating mutations in

AKT1 (D.)

Figure 1

A. B.

Lung Adenocarcinoma 62.9% GI Cancers 17.1% Other 3.5% Thymus carcinoma (nos) 1.7%

Thoracic Cancers Mesothelioma 4.9% Lung SCC Lung NSCLC 20.7% 10.5% 16.4% Breast Cancers 14.3% Breast Carcinoma (nos) 45.9% Other 2.5%

Gyn Cancers Other 1.3% Endocrine Cancers 1.3% 8.3% Breast Metaplastic Breast IDC Carcinoma 2.0% Melanomas 3.2% Colon Adenocarcinoma (CRC) 45.5% Breast ILC 5.2% 55.9%

H&N Cancers Other 2.0% 4.1% Esophagus SCC 1.7% Duodenom Adeno 1.7% Anus SCC 1.9% Hep-Panc-Bil Small Intestine Adeno 2.0% Neuroendocrine Cancers Stomach Adeno Diffuse Type 2.2% 7.1% GE Junction Adeno Appendix Adeno 3.6% 4.3% 12.4% CNS Cancers Stomach Adeno (nos) Other Carcinomas Rectum Adeno 8.1% GU Cancers 5.4% 8.6% 6.1% 5.6% C. D. 700 60 12000 10000 600 8000 50 SV

CN 6000 RE 500

Mult. 4000 Number of patients 40 2000 400 0 Male Female 30 Number of patients 300 Percent Samples 20 200 100 10 0 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87

0 Age at testing 11 TP53 APC MYC MCL1 RB1 NF1 FGF3FGF4BRAF KRAS PIK3CA PTENARID1AEGFR ERBB2 CCND1 FGF19 SMAD4LRP1BKMT2DFGFR1STK CCNE1 CDKN2ADownloadedCDKN2B from cancerres.aacrjournals.org on September 27, 2021. © 2017 American Association for Cancer Research. Author Manuscript Published OnlineFirst on February 24, 2017; DOI: 10.1158/0008-5472.CAN-16-2479 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Figure 2

BRCA BLCA 100 OVCA UCEC GBM 70 70 ** 60

60 ** ** 60 60

80 ** 50 50 50 50 40 60 40 40 ** 40 ** ** ** 30 30

** 30 30 40 % Individuals % Individuals % Individuals % Individuals % Individuals ** ** 20

20 ** 20 ** ** ** 20 ** 20 ** ** 10 10 10 10 0 0 0 0 0

A3 T6A RB1 NF1 T6A NF1 RB1 KIT TP53 MYC FGF4FGF3 A AT TP53 FGF3FGF4 TP53MYC AKT2A TP53 JAK1 TP53 IDH1TRX FGF19 MCL1 PTENK ESR1 FGF19 MCL1 KRAS MCL1 SOX2 K BRAFMYCL PTEN KRAS CTCF BCOR PTENEGFR CDK4 A MDM2 PIK3CA CCND1 FGFR1 ZNF703ERBB2 G ARID1AKMT2DKDM6A PIK3CAFGFR3ERBB2CCND1 CCNE1BRCA1 PIK3CA BRCA2 FGF12 PIK3CA ARID1A PIK3R1 KMT2D FBXW7 ERBB2CCNE1 PIK3CA PIK3R1 CDKN2ACDKN2B CTNNB1PPP2R1A CDKN2ACDKN2B PDGFRA

100 COAD SKCM STAD LUAD HNSC

** 80 60

50 ** ** ** 60 ** 80 50 60 50 40 ** 40

60 **

** ** 40 30

** 40 30 ** 30 40 ** % Individuals % Individuals % Individuals % Individuals % Individuals 20 ** ** **

** 20 20 ** ** **

** 20 20

10 ** 10 10 0 0 0 0 0 1 T3 1 L NF1 KIT NF1 OR APCTP53 MYC F TP53 MYC TP53 MYC APC TP53 MYC T TP53 KRAS BRAFPTEN CDK8 GNAS BRAFNRAS PTENARID2 CDH1KRAS PTEN KRASEGFR STK MCL1 MDM2 FGF4FGF3 SOX2EGFR PTEN PIK3CASMAD4FBXW7 ARID1A AMER1 LRP1B ARID1A PIK3CA ARID1A ERBB2PIK3CA KMT2DFGFR2CCND1 FBXW7 NFKBIAERBB2 LRP1B CCND1PIK3CAFGF19 KMT2D LRP1B ZNF217 CDKN2A CDKN2B GRIN2A DNMT3A CDKN2A CDKN2B CDKN2A CDKN2BNKX2-1* RIC CDKN2A CDKN2BNOTCH1 NOTCH2

FMI Short variants (SV) Copy number (CN) alterations Both TCGA (SV+CN)

137 R361C/H/S/fs*12 #Mutations 0 MH1 MH2

B. 0 100 200 300 400 500 552 aa SMAD2

16 S464* #Mutations 0 MH1 MH2 C. 0 100 200 300 400 467 aa TGFBR2

32 R528C/H # Mutations# 0 ecTbetaR2 Pkinase_Tyr

0 100 200 300 400 500 567 aa

D. Distribution of TGFβ/SMAD Pathway Alterations E.

SMAD4 Multiple Alts. TGFBR2 Alt. SMAD2 SMAD2 Alt. SMAD4 Alt. TGFBR2 % Individuals% SMAD4 Alt. SMAD2 Alt. TGFBR2 Alt.

G. 0 5 10 15 20

GI GU H&N Gyn CNS Breast Thoracic Other Ca. Germ Cell Endocrine UnclassifiedMelanomas Hep-panc-bil Mesenchymal Neuroendocrine Non-melanoma Skin

F. PFS Probability PFS SMAD

KRAS

TP53 TGFβ/SMAD Pathway-Unalt. TGFβ/SMAD Pathway-Alt.

SMAD4 Alt. SMAD2 Alt. TGFBR2 Alt. 0.0 0.2 0.4 0.6 0.8 1.0 Downloaded from cancerres.aacrjournals.orgKRAS TP53 on September 27,0 2021. © 20171000 American Association 2000 for Cancer 3000 4000 Multiple TGFβ/SMAD Pathway Alts Alt. Alt. Research. Time (days) NOTCH1 C. Figure 4 B. A. BCOR # Mutations # Mutations 0 5 0 5 Downloaded from 0

0 Percent Samples 0 5 10 15 20 25

NOTCH1 Author manuscriptshavebeenpeerreviewedandacceptedforpublicationbutnotyetedited. Author ManuscriptPublishedOnlineFirstonFebruary24,2017;DOI:10.1158/0008-5472.CAN-16-2479

BCOR KDM6A

TP53 CREBBP 400

KIT ARID1A cancerres.aacrjournals.org KMT2D

RUNX1 400 Number of Samples 100 120 140

PIK3CA 20 40 60 80 Number of NOTCH1 mutations perNumbermutations NOTCH1sample of 0 PDGFRA NOTCH1 mutations/sampleNOTCH1 0 KDR 800 EP300 1

Missense SPEN

BAP1

PIK3R1 2

A CDKN2ATM SMARCB1 3 800 1200 In-frame

Research. SMARCA4

CCND1 SV &CN RE CN SV Truncation MCL1 1600 1200 BCOR 2000 Ank_2 1600 2400 PUFD 2555 aa 1755 aa Author Manuscript Published OnlineFirst on February 24, 2017; DOI: 10.1158/0008-5472.CAN-16-2479 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Figure 5

A. Novel tyrosine kinase fusions in lung adenocarcinoma/NSCLC C. Novel RAF fusions in known diseases c2orf34 ex 1-3 ALK ex 20- 29

astrocytoma TPR ex 1-14 BRAF ex 7-18 MPRIP ex 1-21 ALK ex 20-29

skin melanoma CUX1 ex 1-7 RAF1 ex 6-17 TNIP2 ex 1-5 RET ex 12-19

unknown primary melanoma PRKAR2A ex 1-8 RAF1 ex 8-17 PICALM ex 1-19 RET ex 12-19 prostate acinar cell carcinoma INADL ex 1-31 RAF1 ex 8-17 MYO5C ex 1-30 ROS1 ex 35-43

GRIPAP1 ex 1-22 NTRK1 ex 11-17

Novel tyrosine kinase fusion in papillary thyroid carcinoma B. Novel RAF fusions in novel diseases SATB1 ex 1-7 RET ex 12-19

CRC DHX9 ex 1-3 RAF1 ex 8-17 D. breast carcinoma (nos) GOLGA4 ex 1-22 RAF1 ex 8-17 Novel kinase fusions in novel diseases CRC CENPF ex 1-11 pancreatic ductal adeno PDZRN3 ex 1-7 RAF1 ex 8-17 ALK ex 20-29 GI neuroendocrine pancreatic neuroendocrine ALCAM ex 1-14 RAF1 ex 6-17 GPHN ex 1-9 RET ex 12-19x

lung adenocarcinoma PRKAR2A ex 1-4 RAF1 ex 8-17 uterine endometrial carcinoma LRRC71 ex 1 NTRK1 ex 8-17 Downloaded from cancerres.aacrjournals.org on September 27, 2021. © 2017 American Association for Cancer Research. Author Manuscript Published OnlineFirst on February 24, 2017; DOI: 10.1158/0008-5472.CAN-16-2479 Figure 6 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

A. B. 100 Multiple 1200 BRAF Any HRAS Any NRAS Any KRAS Amp

1000 KRAS SV/SV&Amp 80 800 60 600 % samples # samples 40 400 200 20 0 0

GEJ KRAS ex 2 Colon Liver Rectum Small Pancreas Appendix Bile Duct Stomach Extended RAS Complete RAS Ductal Intestine Gallbladder C. D. E. EGFR Exon 19 Deletions EGFR Exon 20 Insertions ERBB2 Insertions 150 20 744 763 772 72 135 764 18 745 64 765 773 120 16 746 766 56 105 774 767 14 48 747 775 90 768 12 769 40 748 75 776 770 10 60 32 749 771 8 777 772 45 24 750 773 6 778 16 30 774 751 4 779 15 775 8 752 776 2 780 321 4 5 6 7 8 0 321 4 5 0 321 4 0 Deletion Length Insertion Length Insertion Length

Figure 7

A. B. ERBB2 CDK4/6 amplification 25 25 20 20 15 15 % Individuals % Individuals 10 10 5 5 0 0

GBM Adeno. Adeno. Adeno. Adeno. Adeno. Adeno. Adeno. Adeno. Adeno. Adeno. Breast IDC Breast ILC GEJ Adeno. (nos) Adeno (nos) GEJ Cervix Lung Astrocytoma Bile Duct Skin Melanoma Breast Carc. (nos) Adenocarc. (nos) rasitional Cell Carc.GallbladderDuodenum Gallbladder Oligodendroglioma T Lung NSCLC (nos) Stomach Ovary Clear Cell KidneyCarc. Urothelial Carc. Small Intenstine Breast Metaplastic Carc. Salivary Gland Carc. (nos) Stomach Salivary Gland Carc. (nos) Unknown Primarmy Adrenal Gland Cortical Carc. Unknown Primary Melanoma Uterus Endometrial

Bladder Urothelial Uterus Endometrial Papillary Serous Carc. SV CN Both

C. MET D. AKT1 (E17K) 10 10 8 8 6 6 % Individuals % Individuals 4 4 2 2 0 0

ype T GBM Adeno Adeno Adeno. Adeno. Adeno. HNSCC fuse Liver HCC Lung SCC Breast ILC Breast IDC Lung SCC GEJ Lung Adeno (nos) Adeno. (nos) Adeno. (nos) Acinar Adeno. (CRC) Adeno. (CRC) ferentiated Carc. Unknown Primary f Lung NSCLC (nos) Breast Carc. (nos) Adeno. Dif ransitional Cell CarcColon Stomach T Stomach Rectum Kidney Clear Cell Carc. Unknown PrimaryProstate SCC Unknown Primary Carc. Unknown Primaryl Liver Cholangiocarcinoma Ovary Epithelial Carc. (nos) Stomach Uterus Endometrial Lung Large Cell Neuroendocrine Uterus Endometrial Papillary Serous Uterus Endometrial Endometrioid Lung Small Cell Undi Bladder Urothelial

High-throughput genomic profiling of adult solid tumors reveals novel insights into cancer pathogenesis

Ryan J Hartmaier, Lee Albacker, Juliann Chmielecki, et al.

Cancer Res Published OnlineFirst February 24, 2017.

Updated version Access the most recent version of this article at: doi:10.1158/0008-5472.CAN-16-2479

Supplementary Access the most recent supplemental material at: Material http://cancerres.aacrjournals.org/content/suppl/2017/02/24/0008-5472.CAN-16-2479.DC1

Author Author manuscripts have been peer reviewed and accepted for publication but have not yet been Manuscript edited.

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