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Clinical and Genomic Implications of Luminal and Basal Subtypes Across Carcinomas

Shuang G. Zhao 1,*, †, William S. Chen 2,3,*, Rajdeep Das 3, S. Laura Chang 3, Scott A. Tomlins 4, Jonathan

Chou 3, David A. Quigley 3, Ha X. Dang 5, Travis Barnard 3, Brandon A. Mahal 6, Ewan A. Gibb 7, Yang Liu 7,

Elai Davicioni 7, Linda R. Duska 8, Edwin Posadas 9, Shruti Jolly 1, Daniel E. Spratt 1, Paul L. Nguyen 6,

Christopher A. Maher 5, Eric J. Small 10, Felix Y. Feng 3,10,11

1Department of Radiation Oncology, 4Department of Pathology, University of Michigan 2Yale School of Medicine 3Department of Radiation Oncology, 10Department of Medicine, 11Department of Urology, University of

California San Francisco 5McDonnell Genome Institute, Department of Internal Medicine, Washington University in St. Louis 6Department of Radiation Oncology, Dana-Farber Cancer Institute 7GenomeDx Biosciences Inc. 8Department of Obstetrics and Gynecology, University of Virginia 9Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai

*These authors contributed equally †Corresponding author

Shuang (George) Zhao

1500 E. Med Ctr Drive, Ann Arbor, MI, 48109

E-mail: [email protected]

Running Title: Luminal and Basal Subtypes Across Carcinomas

Disclosures

EAG, YL, and ED are employees of GenomeDx Biosciences. SGZ, FYF, and GenomeDx Biosciences have

filed a patent application on luminal and basal subtypes in prostate cancer. SGZ has received travel/expenses

from GenomeDx Biosciences.

Disclosures unrelated to the content of this manuscript: SGZ, FYF, and SLC have patent applications with

GenomeDx on other work. SGZ and FYF have a patent application with Celgene. FYF is a founder, and SLC is

an employee of PFS Genomics. FYF has consulted for Dendreon, Sanofi Genzyme, Ferring, EMD Serono,

Janssen, Bayer, and Clovis.

Abstract

Background: Carcinomas originate from epithelial tissues, which have apical (luminal) and basal orientations.

The degree of luminal versus basal differentiation in cancer has been shown to be biologically important in

some carcinomas and impacts treatment response.

Experimental Design: While prior studies have focused on individual cancer types, we used a modified clinical-

grade classifier (PAM50) to subtype 8764 tumors across 22 different carcinomas into luminal A, luminal B, and

basal-like tumors.

Results: We found that all epithelial tumors demonstrated similar gene expression-based luminal/basal

subtypes. As expected, basal-like tumors were associated with increased expression of the basal markers

KRT5/6 and KRT14, and luminal-like tumors were associated with increased expression of the luminal markers

KRT20. Luminal A tumors consistently had improved outcomes compared to basal across many tumor types,

with luminal B tumors falling between the two. Basal tumors had the highest rates of TP53 and RB1 mutations

Research. on March 18, 2021. © 2018 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

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and copy number loss. Luminal breast, cervical, ovarian, and endometrial tumors had increased ESR1

expression, and luminal prostate, breast, cervical, and bladder tumors had increased AR expression.

Furthermore, Luminal B tumors had the highest rates of AR and ESR1 mutations and had increased sensitivity

in-vitro to bicalutamide and tamoxifen. Luminal B tumors were more sensitive to gemcitabine, and basal tumors

were more sensitive to docetaxel.

Conclusions: This first pan-carcinoma luminal/basal subtyping across epithelial tumors reveals global

similarities across carcinomas in the transcriptome, genome, clinical outcomes, and drug sensitivity,

emphasizing the biological and translational importance of these luminal vs. basal subtypes.

Statement of Significance

Carcinomas have historically been classified by histology and anatomic site-of-origin. We present the first pan-

carcinoma luminal/basal subtyping across 8764 samples from 22 epithelial tumors from the TCGA, revealing

global similarities in the transcriptome, genome, clinical outcomes, and drug sensitivity which emphasize the

biological and translational importance of these subtypes.




Introduction

By definition, epithelial tissues all have apical (luminal) and basal orientations (1). Tumors originating from

epithelial tissues (e.g. carcinomas) may reflect this dichotomy with relative degrees of luminal or basal

differentiation (1, 2). Understanding this key biological difference is important because the luminal-ness or

basal-ness of a particular tumor may impact both overall prognosis and response to treatment. Clinically

important luminal and basal subtypes of several different carcinomas have previously been described. The

PAM50 subtyping is a clinical-grade luminal-basal classifier which has been used to group breast cancers into

Luminal A (LumA), Luminal B (LumB), Basal, and Her2-like subsets (3, 4). The luminal breast cancer subtypes

express higher levels of ER and PR and are more responsive to hormonal therapy (5). The luminal and basal

subtypes of prostate cancer were also recently described using a slightly modified PAM50 algorithm (6).

Analogous to breast cancer, the luminal subtypes of prostate cancer exhibited higher expression of AR and

LumB-like tumors preferentially benefited from androgen deprivation therapy (6). Bladder cancers also

demonstrate luminal and basal subtypes, which predict response to front-line chemotherapy (7).

Although carcinomas have historically been classified and treated primarily based on their histology and

anatomic site of origin, there is reason to believe that a pan-carcinoma classification schema such as PAM50

could have utility across a number of cancer types independent of site of origin. Although the landmark

publication of The Cancer Genome Atlas (TCGA) pan-cancer atlas demonstrated that the cell-of-origin patterns

dominate the biological differences between cancers (8), commonalities were noted within gynecologic/breast

cancers (9), gastrointestinal adenocarcinomas (10), and squamous cell carcinomas (11). Furthermore,

numerous common biological axes transcending tumor type were found, including driver mutations (12),

oncogenic signaling pathways (13), DNA repair defects (14), metabolomics subtypes (15), immunity (16), and

stem-ness (17). To our knowledge, no pan-cancer RNA-based subtypes have yet been identified.

We hypothesized that luminal/basal subtypes represent an important and clinically meaningful measure of

tumor biology that transcends cancer type. In order to test this, we utilized a modified PAM50 (6) to classify

8764 tumors across 22 different tumor types into luminal and basal subtypes. In the first pan-carcinoma study

of its kind, we show that luminal and basal subtypes exist for of all epithelial-derived tumors, and that these

subtypes exhibit different patterns of expression, genomic alterations, clinical outcomes, and response to

therapy.

Methods

Luminal and basal subtyping

Subtyping into luminal and basal subtypes was performed using the original PAM50 algorithm (18)

(Supplemental Table 1). While other luminal/basal subtyping algorithms exist, PAM50 is the only one which

has been developed into a commercial clinical test (18), and which has been demonstrated to work in multiple

tumor types (4, 6, 7). Source code was downloaded from the University of North Carolina Microarray Database

(https://genome.unc.edu/pubsup/breastGEO/) and run without modification as has been performed in other

tumor types (3, 6). Since the majority of epithelial tumors are not known to be HER2 driven, we excluded the

HER2 subtype and instead only LumA, LumB, and Basal subtypes were assigned as previously described (6).

Gene Set Enrichment Analysis

Identification of genes correlated with subtypes was performed by first assessing Spearman’s correlation for

each gene with the LumA, LumB, and Basal-ness scores from the PAM50 algorithm. Additional subtype-

specific genes were identified by selecting genes with a Spearman’s rho ≥0.4 for one and a multiple-testing

adjusted p-value (FDR) ≤ 0.05, and with one subtype, and Spearman’s rho ≤0.2 with the other two subtypes.

The correlation coefficients were then input to Gene Set Enrichment Analysis (GSEA) pre-ranked. The

Hallmark Epithelial-Mesenchymal Transition (EMT) gene set was used, as well as a custom gene set for the



https://genome.unc.edu/pubsup/breastGEO/


nuclear hormone receptor family obtained from the HUGO Gene Nomenclature Committee

(www.genenames.org/cgi-bin/genefamilies/set/71).

Genomic data

TCGA pan-cancer data were obtained via the UCSC Xena Browser. The GDC HTSeq FPKM RNAseq dataset,

the Mutect2 somatic mutation dataset, and the Affymetrix SNP Array 6.0 masked copy number segment

dataset were downloaded for analysis (19). All datasets comprising epithelial tumors (carcinomas) were

included (ACC [adrenocortical carcinoma], BLCA [bladder urothelial cancer], BRCA [breast cancer], CESC

[cervical squamous cell cancer], CHOL [cholangiocarcinoma], COAD [colon adenocarcinoma], ESCA

[esophageal carcinoma], HNSC [head & neck squamous cell carcinoma], KIRC [renal cell carcinoma], KIRP

[renal papillary cell carcinoma], LIHC [hepatocellular carcinoma], LUAD [lung adenocarcinoma], LUSC [lung

squamous cell carcinoma], MESO [mesothelioma], OV [ovarian serous cystadenocarcinoma], PAAD

[pancreatic adenocarcinoma], PRAD [prostate adenocarcinoma], READ [rectal adenocarcinoma], STAD

[gastric adenocarcinoma], THCA [thyroid carcinoma], THYM [thymoma], UCEC [endometrial carcinoma]).

Cutaneous carcinomas were not included in TCGA and thus are not represented. Mutations were counted if

they were exonic and non-silent if in a coding gene. Copy number (CN) gain was defined as Log2(CN/2)≥1.

Copy number loss was defined as shallow: Log2(CN/2)≤-1 or deep: Log2(CN/2)≤-2. TCGA proliferation scores,

mutation rates, fraction altered, and aneuploidy scores were previously published (16). Comparison of luminal

and basal markers was performed by first mean-centering Log2(FPKM+1) and scaling by the standard

deviation to generate a Z-score of each gene within each individual cancer type. This standardization was

performed independently for each cancer type because gene expression ranges could vary.

Drug response

Cancer cell line Affymetrix Human Genome U219 array gene expression and drug response data were

obtained from the Genomics of Drug Sensitivity in Cancer (GDSC) project (20) (www.cancerrxgene.org). Drug

response was assessed using the IC50. Two dose ranges for bicalutamide were available, and the dosages

that had more response data from cell lines from hormone-responsive tumors was selected (0.039-10µM).

Statistical methods

Overall survival was the primary clinical outcome in the TCGA pan-cancer data, as it was available for all tumor

types. All carcinomas were included in the above genomic analyses, but we excluded breast cancer from

clinical analysis given the long natural history of the disease and the limited follow-up in TCGA, and the fact

that the prognostic implications of the PAM50 subtypes of breast cancer have been extensively explored in the

literature in more appropriate cohorts (3-5, 18). The TCGA prostate cancer cohort faces a similar issue of a

long natural history and limited follow-up, and has likewise previously been investigated in large clinical cohorts

(6). We also excluded thymoma, and thyroid cancer from the clinical analyses due to very low event rates

suggesting similar issues. We excluded cholangiocarcinoma from clinical analysis given the small number of

patients with outcomes available (N=45). Comparison of continuous variables across subtypes was performed

using ANOVA, with a post-hoc Tukey test to examine individual groups. Comparison of categorical variables

across subtypes was performed using Fisher’s exact test. All analyses performed in using R version 3.4.4. All

statistical testing was two-sided, and a p-value ≤ 0.05 was considered significant. Multiple testing correction

was performed using the Benjamini-Hochberg procedure.

Results

We first subtyped 8764 TCGA pan-cancer tumor samples across 22 carcinoma types into LumA, LumB, and

basal-like subtypes using the PAM50 clustering algorithm (Figure 1A, Supplemental Table 2, Supplemental

Figure 1). The gene expression patterns in all tumor types were roughly consistent with the patterns seen in

breast cancer. The frequently used basal markers of KRT5/6 (average of KRT5, KRT6A-C) (1) and KRT14 (21,

22) were both significantly increased across the basal-like carcinomas (T-test p<0.0001; Figure 1B) and the



http://www.cancerrxgene.org/


luminal marker KRT20 (1) was significantly increased across the luminal-like carcinomas (T-test p<0.0001;

Figure 1C). GSEA revealed that the Hallmark EMT gene signature was most correlated with Basal-ness

(Normalized Enrichment Score (NES) = 1.87 for Basal vs. 1.45 for LumA and -2.67 for LumB) consistent with

the literature in breast (23) and prostate (24) cancer (Supplemental Figure 2). Pan-carcinoma proliferation

scores were also modestly higher in basal-like tumors compared to LumB-like tumors (ANOVA p<0.0001,

Tukey p=0.0015), and both were much higher compared to LumA-like tumors (Tukey p<0.0001 for both),

consistent with other tumor types (3, 6) (Supplemental Figure 1). Additional subtype-specific genes are shown

in Supplemental Table 3. Silhouette scores (a measure of cluster fit (25)) were highest in breast cancer as

expected (Supplemental Figure 3).

Clinical outcomes

For each cancer type, we then examined clinical outcome differences between the subtypes. In eight (ACC,

KIRC, KIRP, LIHC, LUAD, MESO, PAAD, UCEC) out of 17 different tumor types analyzed (5 of the initial 22

were not included in the clinical analysis, see methods), we found that patients with basal-like tumors had

significantly worse survival (FDR q<0.05) compared to patients with LumA-like tumors, with LumB-like tumors

falling between the two, akin to what has been reported for breast cancer (3) (Figure 2A-H). No LumA

subgroup had significantly worse survival than basal in any cancer type.

Mutation patterns

We next explored differences in mutational profiles between subtypes. Overall, LumA-like carcinomas had

lower mutation rates, fraction altered, and aneuploidy scores than LumB or basal-like carcinomas (ANOVA

p<0.0001, Tukey p<0.0001; Supplemental Figure 4, Supplemental Table 4). When we performed an unbiased

ranking of all genes with an overall mutation rate ≥1% using Fisher’s exact multiple testing adjusted p-values

(FDR q-values), we found that the top two most differentially mutated genes were TP53 and RB1 (Figure 2I).

TP53 mutation frequency was highest in the basal-like subtype overall (49.5% in basal, 25.0% in LumA, 36.0%

in LumB, FDR q<0.0001), as well as in 15 out of 22 individual tumor types (Figure 2J). RB1 mutation frequency

was also highest in basal-like tumors overall, though only slightly less than in LumB-like tumors (5.9% in Basal,

1.9% in LumA, 5.6% in LumB, FDR q<0.0001). RB1 mutations were least frequent in LumA-like tumors overall

as well as in 12 out of 19 individual tumor types with RB1 mutations and were tied for least frequent in 4 others

(Figure 2K). These results remain similar after accounting for inactivation by deep deletion (Supplemental

Figure 5-6). The full results for genes with differential rates of deep deletion can be found in Supplemental

Table 5.

Hormone receptors

Luminal subtypes have been shown to express higher levels of hormone receptors and respond better to

hormonal therapy in hormonally-driven tumors (3, 5, 6). In breast cancer, as expected (3, 4), luminal tumors

expressed ESR1 at higher levels (ANOVA p<0.0001; Figure 3A), and LumA-like tumors expressed PGR at the

highest levels (ANOVA p<0.0001; Figure 5A). Interestingly, luminal breast tumors also expressed AR at higher

levels (ANOVA p<0.0001; Figure 3A), consistent with prior publications (26). Surprisingly, luminal cervical

squamous tumors demonstrated the same patterns of expression for ESR1, PGR, and AR (ANOVA p<0.0001,

p=0.0001, p=0.0004 respectively, Figure 3A), providing additional evidence that hormonal receptors may play

a role in cervical cancer (27, 28). We found that, similar to breast cancer, other female reproductive cancers

such as ovarian and endometrial cancers (Figure 3B) likewise expressed ER at higher levels (ovarian: ANOVA

p<0.0001; endometrial: ANOVA p<0.0001), and LumA-like tumors expressed PR at higher levels in

endometrial cancer (ANOVA p<0.0001). Analogously, we found that luminal-like prostate tumors expressed AR

at higher levels compared to basal-like tumors (ANOVA p<0.0001; Figure 3C), which is supported by existing

literature (6). A small percentage of bladder tumors are also known to express AR (29), and we found that

luminal-like bladder tumors also express AR at higher levels than basal tumors (ANOVA p<0.0001; Figure 3C).

We performed a global analysis of nuclear hormone receptors using GSEA and found a strong positive




association only with LumA (NES = 2.39) and negative associations with LumB (NES = -2.32) and Basal (NES

= -2.02) (Supplemental Figure 2).

Exploratory drug response

We sought to determine whether the biological differences between basal and luminal subtypes could confer

differing sensitivity to specific treatments. In the GDSC cell line drug response data, we grouped 421

carcinoma cell lines into the same luminal and basal subtypes (Figure 1A, Supplemental Table 6) and

compared the response across subtypes in four hormonally-driven tumors commonly treated with anti-

hormonal therapies (breast, prostate, ovarian, and endometrial cancer). We found that in cell lines from these

tumors, LumB-like tumors were preferentially sensitive to both tamoxifen (ANOVA p=0.043, endometrial cancer

excluded since tamoxifen is a partial agonist; Figure 3D) and bicalutamide (ANOVA p=0.0028; Figure 3D).

Interestingly, when we examined mutations and CN gains for AR and ESR1 across carcinomas, we found that

AR and ESR1 mutations or CN gains were more frequent in LumB-like tumors (p=0.049 and p=0.008

respectively (Figure 3F), including in hormonally driven tumors (Supplemental Figures 7-8). The full results for

genes with differential rates of CN gain can be found in Supplemental Table 7.

We also examined drug response data across all carcinoma cell lines for 11 chemotherapeutic agents in

GDSC that are used in clinical practice to treat carcinomas. After accounting for multiple testing, we found that

gemcitabine and docetaxel had significant differences in drug response between subtypes (ANOVA FDR

q<0.05). LumB-like cell lines showed increased sensitivity to gemcitabine whereas basal cell lines showed

increased sensitivity to docetaxel (Figure 4). These results suggest that the biological differences between

subtypes may have clinical implications and provide preliminary evidence that these subtypes may be

important in selecting therapies in patients with cancer.

Discussion

Herein, we describe the first luminal-basal molecular classification scheme applied broadly across a broad

array of carcinomas and demonstrate that luminal and basal subtypes are present across all tumor histologies

regardless of site of origin. We show that across cancer types, there are consistent differences in gene

expression, mutation/CN alteration patterns, and clinical outcomes between molecular subtypes. Our

preliminary data suggest that these differences may result in differing sensitivities to specific therapies. The

basal markers KRT5/6, KRT14, and the luminal marker KRT20, are concordant with these subtypes across

carcinomas. Furthermore, the pan-carcinoma patterns of TP53 and RB1 mutation/CN loss reflect previously

reported findings in breast and bladder cancer (30, 31). The proliferation score pattern also matches what is

found in breast and prostate cancer (3, 6). The consistency of our findings across cancer types with the

published literature in breast and bladder cancer supports the biological validity of these subtypes.

Furthermore, we have shown that these molecular subtypes predict clinical outcome. In eight tumor types,

patients with basal-like tumors had significantly worse survival compared to LumA-like tumors, with LumB-like

tumors falling somewhere in-between, matching prior reports in breast cancer and bladder cancer. In breast

cancer, LumA-like tumors tend to have better outcomes than LumB-like tumors, and both tend to have better

outcomes that basal-like tumors, though with longer term follow-up out to 10 years, the LumB-like outcomes

converge with basal tumors (32). In bladder cancer, the luminal-like tumors likewise have better outcomes than

basal-like tumors (31). These results are likely driven in part by the strong difference in the proliferation-related

genes between the LumA-like and basal-like tumors. However, while LumB-like tumors have similar

proliferation scores to basal-like tumors, they do not always have similar survival or mutational or gene

expression profiles indicating other important biological differences.

Luminal and basal subtypes are perhaps best known for their implications in treatment response for hormonal

therapies. Luminal breast cancers have been shown to respond preferentially to anti-estrogen therapies (5).




More recently, LumB-like prostate cancers have been shown to respond preferentially to anti-androgen

therapies (6), and this is now being tested in a randomized national trial (clinicaltrials.gov ID: NCT03371719).

In our exploratory cell line drug response analysis, we found that LumB-like hormonally-driven tumors overall

responded better to both anti-estrogen (tamoxifen) and anti-androgen (bicalutamide) therapies, and LumB-like

tumors also had globally increased rates of mutation or CN gain of ESR1 and AR. This is suggestive that these

subtypes may have potential in selecting patients who preferentially benefit from anti-hormonal therapies in

other hormonally-driven tumors such as endometrial and ovarian cancer.

Luminal and basal subtypes have also been implicated in treatment response to cytotoxic chemotherapies. In

breast cancer, the basal subtype has been shown to especially benefit from taxane therapy (33), consistent

with our exploratory pan-carcinoma drug sensitivity results. Basal-like bladder tumors have been shown to

preferentially benefit from several other chemotherapies (7), though we did not observe this globally in our cell

line data. LumA-like metastatic breast cancers have also been shown to benefit less from gemcitabine with

carboplatin (34), consistent with our cell line results, though other trials have shown that the benefit of

gemcitabine is primarily in basal-like breast cancers (35). Nonetheless, our exploratory analysis would suggest

that the luminal and basal subtypes across carcinomas may respond differently to chemotherapies.

This study is not without limitations. We are unable account for the effect of tumor heterogeneity, as the TCGA

performed bulk tumor sequencing, and does not include single-cell RNAseq data. Similarly, bulk sequencing

also includes other cell types such as stroma, vasculature, immune infiltrate, etc. which can affect the gene

expression. However, this is an inherent limitation of all cancer subtyping efforts performed on bulk sequencing

of tumors to date, and is not unique to our study. The cell line data should be minimally affected by these

issues.

Current systemic treatment of solid tumors is largely driven by histology and site of origin. However, the

completion of the landmark TCGA sequencing efforts has revealed that there are many commonalities

between neoplasms that transcend organ sites (12-17). We demonstrate that many epithelial tumors, including

but also extending well beyond breast, bladder, and prostate cancer, have luminal and basal subtypes which

are biologically and clinically meaningful. Observations such as this are the impetus behind moving towards a

new paradigm in oncology where molecular information is used to subgroup and ultimately target treatments

for cancer patients.

Acknowledgements

We would like to acknowledge the assistance of Steven Kronenberg with graphic design of the figures. SGZ,

BAM, DAQ, EJS and FYF are supported by the Prostate Cancer Foundation.

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Figure legends

Figure 1: Luminal and basal subtypes of carcinoma

(A) Heatmaps show similar patterns of expression for the luminal and basal subtypes of all carcinomas

available in the TCGA. Rows are genes and columns are samples. Red means higher expression and green

means lower expression. Genes are ordered to arrange the luminal genes, the proliferation genes, and the

basal genes in groups from top to bottom in breast cancer. In the bar above each heatmap: Red = Basal; Dark

Blue = LumA; Light Blue = LumB. ACC = adrenocortical carcinoma; BLCA = bladder urothelial cancer; BRCA =

breast cancer; CESC = cervical squamous cell cancer; CHOL = cholangiocarcinoma; COAD = colon

adenocarcinoma; ESCA = esophageal carcinoma; HNSC = head & neck squamous cell carcinoma; KIRC =

renal cell carcinoma; KIRP = renal papillary cell carcinoma; LIHC = hepatocellular carcinoma; LUAD = lung

adenocarcinoma; LUSC = lung squamous cell carcinoma; MESO = mesothelioma; OV = ovarian serous

cystadenocarcinoma; PAAD = pancreatic adenocarcinoma; PRAD = prostate adenocarcinoma; READ = rectal

adenocarcinoma; STAD = gastric adenocarcinoma; THCA = thyroid carcinoma; THYM = thymoma; UCEC =

endometrial carcinoma. GDSC = Genomics of Drug Sensitivity in Cancer (cell lines). The 50 genes are ordered

the same across all heatmaps, and generally fall within three groups. The top group represents genes which

are expressed higher in LumA and LumB, and lower in Basal tumors. The middle group represents genes

which are expressed higher in Basal and LumB, and lower in LumA. The bottom group represents genes which

are expressed higher in Basal and LumA, and lower in LumB.

(B) Boxplots show that the basal markers of KRT5/6, and KRT14 were more highly expressed in the basal-like

carcinomas. (C) The luminal marker KRT20 was more highly expressed in the luminal-like carcinomas. The y-

axis represents the average Z-score from the Log2(FPKM+1) within each tumor type. Note that outliers were

not shown in this plot due as the large range that made it difficult to visualize the box and whiskers, but were

retained for the statistical inference. T-test: *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001.

Figure 2: Associations with survival and genomic alterations

(A-H) Kaplan-Meier curves of overall survival in the 8 out of 17 tumor types where there was a significant

difference between a luminal versus basal subtype with a multiple-testing adjusted FDR q < 0.05. Basal tumors

have worse outcomes compared to LumA tumors, with LumB tumors between the two, depending on the

cancer type. (I) Scatter plot showing the -Log10 FDR q-values using Fisher’s exact test for mutation frequencies

of each gene between the subtypes show that TP53 and RB1 are the top 2 differentially mutated genes. Bar-

plots of the mutation frequencies for TP53 (J) and RB1 (K) are shown both across carcinomas and in each

tumor type individually.

Figure 3: Associations with hormone receptors

Boxplots showing that (A) ESR1 and PGR expression are highest in luminal breast cancers, with PGR being

highest in luminal A. AR is also higher in luminal breast cancers. In cervical cancer, a similar pattern of

expression is seen as in breast cancer. (B) In ovarian and endometrial cancer, ESR1 is higher in luminal




tumors. PGR is higher in luminal A endometrial cancer. (C) In prostate and bladder cancer, AR is higher in

luminal tumors. (D) In cell lines from hormone-driven tumors from the GDSC, box-plots showing the IC50s for

tamoxifen and bicalutamide are lowest in LumB tumors. Tamoxifen was not investigated in endometrial cancer

as it is a partial agonist at the uterus rather than an ER-antagonist. (E) Barplots show that mutation + CN gain

rates across carcinomas for AR (E) and ESR1 (F) are both highest in LumB tumors. Post-hoc Tukey: #p<0.1;

*p<0.05; **p<0.01; ***p<0.001; ****p<0.0001.

Figure 4: Exploratory drug resistance

Across carcinoma cell lines from the GDSC, box-plots showing the IC50s for gemcitabine and docetaxel, the

two drugs out of 11 cytotoxic chemotherapies tested with ANOVA FDR q<0.05. LumB-like cell lines are most

sensitive to gemcitabine and basal-like cell lines are most sensitive to docetaxel. Post-hoc Tukey: *p<0.05;

**p<0.01; ***p<0.001; ****p<0.0001.




Figure 1

****

−3

−2

−1

0

1

2

3

Basal (N=2574) Luminal (N=6190)KRT5/6

**** ****

Z−score (Log

2 FPKM)

−3

−2

−1

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Basal (N=2574) Luminal (N=6190)KRT14

Z−score (Log

2 FPKM)

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Basal (N=2574) Luminal (N=6190)KRT20

Basal Markers Luminal Marker

Z−score (Log

2 FPKM)

B

A

C

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GDSC

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requency

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ACC BLCA BRCA CESC CHOL COAD ESCA HNSC KIRC KIRP LIHC LUAD LUSC MESO OV PAAD PRAD READ STAD THCA THYM UCEC

Muta

tion F

requency

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0.00

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tion F

requency

0.0

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Muta

tion F

requency

Subtype

Basal

LumA

LumB

Subtype

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LumB

RB1

0

10

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-Log10(FDR)

TP53

RB1

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I J

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Months

0 12 24 36 48 60

OS

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22 18 10 7 3 1

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16 16 9 5 3 3

ACC

Months

0 12 24 36 48 60

175 134 105 83 65 39

269 233 186 158 126 98

153 131 114 95 72 52

KIRC

Months

0 12 24 36 48 60

113 89 53 33 26 13

133 112 82 54 45 34

72 62 34 27 21 16

KIRP

Months

0 12 24 36 48 60

80 48 26 17 11 6

204 165 95 64 44 28

134 93 50 34 25 19

LIHC

Months

0 12 24 36 48 60

150 108 63 38 23 15

259 214 124 82 47 32

134 105 57 35 21 13

LUAD

Months

0 12 24 36 48 60

22 9 2

42 31 19 11 7

12 4 2 1

MESO

Months

0 12 24 36 48 60

54 34 9 2 1 1

84 56 21 13 8 6

43 27 8 5 2 1

Basal

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LumB

PAAD

Months

0 12 24 36 48 60

186

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143

109

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159

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Published OnlineFirst December 20, 2018.Clin Cancer Res Shuang G Zhao, William S Chen, Rajdeep Das, et al. Subtypes Across CarcinomasClinical and Genomic Implications of Luminal and Basal

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