Association Between Molecular Subtypes of Colorectal Tumors and Patient Survival, Based on Pooled Analysis of 7 International Studies

Amanda I Phipps; Elizabeth Alwers; Tabitha Harrison; Barbara Banbury; Hermann Brenner; Peter T Campbell; Jenny Chang-Claude; Daniel Buchanan; Andrew T Chan; Alton B Farris; Jane C Figueiredo; Steven Gallinger; Graham G Giles; Mark Jenkins; Roger L Milne; Polly A Newcomb; Martha L Slattery; Mingyang Song; Shuji Ogino; Syed H Zaidi; Michael Hoffmeister; Ulrike Peters

doi:10.1053/j.gastro.2020.02.029

. Author manuscript; available in PMC: 2021 Jun 1.

Published in final edited form as: Gastroenterology. 2020 Feb 20;158(8):2158–2168.e4. doi: 10.1053/j.gastro.2020.02.029

Association Between Molecular Subtypes of Colorectal Tumors and Patient Survival, Based on Pooled Analysis of 7 International Studies

Amanda I Phipps ^1,^2,^*, Elizabeth Alwers ^3,^*, Tabitha Harrison ^2,^*, Barbara Banbury ², Hermann Brenner ^3,^4,⁵, Peter T Campbell ⁶, Jenny Chang-Claude ^7,⁸, Daniel Buchanan ⁹, Andrew T Chan ^10,¹¹, Alton B Farris ¹², Jane C Figueiredo ¹³, Steven Gallinger ¹⁴, Graham G Giles ^15,^16,¹⁷, Mark Jenkins ¹⁶, Roger L Milne ^15,^16,¹⁷, Polly A Newcomb ^1,², Martha L Slattery ¹⁸, Mingyang Song ^10,^19,²⁰, Shuji Ogino ^19,^21,²², Syed H Zaidi ²³, Michael Hoffmeister ^3,^†, Ulrike Peters ^1,^2,^†

¹Epidemiology Department, University of Washington, Seattle, WA

²Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA

³Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany

⁴Division of Preventive Oncology, German Cancer Research Center (DKFZ), National Center for Tumor Diseases (NCT), Heidelberg, Germany

⁵German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ) Heidelberg, Germany

⁶Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA

⁷Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

⁸Cancer Epidemiology Group, University Medical Center Hamburg-Eppendorf, University Cancer Center Hamburg, Hamburg, Germany

⁹Department of Clinical Pathology, Colorectal Oncogenomics Group, The University of Melbourne, Parkville, Victoria, Australia

¹⁰Clinical and Translational Epidemiology Unit, Department of Medicine, and Division of Gastroenterology, Massachusetts General Hospital, Boston, MA

¹¹Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA

¹²Department of Pathology, Emory University, Atlanta, GA

¹³Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California

¹⁴Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada

¹⁵Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia

¹⁶Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia

¹⁷Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia

¹⁸Department of Internal Medicine, University of Utah, Salt Lake City, UT

¹⁹Departments of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA

²⁰Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA

²¹Program in Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA

²²Broad Institute of MIT and Harvard, Cambridge, MA

²³Ontario Institute for Cancer Research, Toronto, Ontario, Canada

Shared first authorship

^†

Shared last authorship

Author Contributions: Drs. Phipps, Alwers, Hoffmeister, and Peters conceived of the original study concept and design. Drs. Phipps, Alwers, and Hoffmeister designed the analyses. The statistical analyses were carried out by Dr. Banbury. Dr. Phipps was assisted in the interpretation of results by Drs. Alwers, Campbell, Buchanan, Chan, Clendenning, Farris, Figueiredo, Gallinger, Giles, Harrison, Milne, Newcomb, Slattery, Song, Ogino, Zaidi, Hoffmeister, and Peters. Dr. Phipps drafted the manuscript and all authors provided critical revisions to the manuscript for important intellectual content. Drs. Campbell, Brenner, Buchanan, Chan, Chang-Claude, Clendenning, Farris, Gallinger, Giles, Milne, Newcomb, Slattery, Song, Ogino, Zaidi, Hoffmeister, and Peters contributed to the acquisition of study data. Drs. Peters, Brenner, and Hoffmeister obtained funding contributing to this manuscript. Dr. Banbury, Ms. Harrison, and Dr. Peters provided administrative, technical, or material support. Study supervision was provided by Drs. Phipps, Harrison, Hoffmeister, and Peters.

^✉

Correspondence: Amanda I. Phipps, Epidemiology Department, University of Washington, 1959 NE Pacific St., Box 357236, Seattle, WA 98195 (aphipps@fhcrc.org), telephone: (206) 667-7741, fax: (206) 667-7850

PMCID: PMC7282955 NIHMSID: NIHMS1568740 PMID: 32088204

Abstract

Background and Aims:

The heterogeneity among colorectal tumors is probably due to differences in developmental pathways and might associate with patient survival times. We studied the relationship among markers of different subtypes of colorectal tumors and patient survival.

Methods:

We pooled data from 7 observational studies, comprising 5010 patients with colorectal cancer. All the studies collected information on microsatellite instability (MSI), CpG island methylator phenotype (CIMP), and mutations in KRAS and BRAF in tumors. Tumors with complete marker data were classified as type 1 (MSI high, CIMP positive, with pathogenic mutations in BRAF but not KRAS), type 2 (not MSI high, CIMP positive, with pathogenic mutations in BRAF but not KRAS); type 3 (not MSI high or CIMP, with pathogenic mutations in KRAS but not BRAF); type 4 (not MSI-high or CIMP, no pathogenic mutations in BRAF or KRAS), or type 5 (MSI-high, no CIMP, no pathogenic mutations in BRAF or KRAS). We used Cox regression to estimate hazard ratios (HR) and 95% CIs for associations of these subtypes and tumor markers with disease-specific survival (DSS) and overall survival (OS) times, adjusting for age, sex, stage at diagnosis, and study population.

Results:

Patients with type 2 colorectal tumors had significantly shorter time of DSS than patients with type 4 tumors (HR_DSS, 1.66; 95% CI, 1.33–2.07), regardless of sex, age, or stage at diagnosis. Patients without MSI-high tumors had significantly shorter time of DSS compared to patients with MSI-high tumors (HR_DSS, 0.42; 95% CI, 0.27–0.64), regardless of other tumor markers or stage, or patient sex or age.

Conclusions:

In a pooled analysis of data from 7 observational studies of patients with colorectal cancer, we found that tumor subtypes, defined by combinations of 4 common tumor markers, were associated with differences in survival time. Colorectal tumor subtypes might therefore be used in determining patients’ prognoses.

BACKGROUND AND CONTEXT:

Studies are needed to determine the association between different subtypes of colorectal tumors and patient survival times.

NEW FINDINGS:

In a pooled analysis of data from 7 observational studies of patients with colorectal cancer, the authors found that tumor subtypes associate with differences in survival time.

LIMITATIONS:

Information regarding how these tumor markers, and tumor marker combinations impact response to specific CRC treatments is relevant, but could not be obtained from this investigation.

IMPACT:

Colorectal tumor subtypes might be used in determining patients’ prognoses or selecting treatment.

Keywords: CRC, mortality, genetics, epigenetic

Lay Summary:

There is a large amount of heterogeneity in genetic and molecular features of colorectal tumors. Analyzing data from 7 large studies, the authors found that patients with different subtypes of colorectal tumors have different survival times.

INTRODUCTION

Colorectal cancer (CRC) is a biologically heterogeneous disease. Such biological heterogeneity reflects diversity in tumor etiology¹ and has implications for survival in patients with CRC.² For example, the presence of microsatellite instability (MSI), stemming from mutated or epigenetically-silenced DNA mismatch repair genes, is a potential driver in CRC development for a subset of tumors (~15%), and has consistently been associated with longer patient survival.³

Colorectal tumors may also develop in the absence of MSI via several plausible pathways: the “traditional” adenoma-carcinoma pathway has been described as demonstrating absent of high level MSI (i.e., non-MSI-high), without the CpG island methylator phenotype (CIMP) or somatic mutations in BRAF and KRAS.¹ Colorectal tumors resulting from a “serrated” pathway has been described as BRAF-mutated and CIMP-positive. An “alternate” pathway has also been suggested for KRAS-mutated colorectal tumors that are non-MSI-high and CIMP-low.^4,5 Unlike MSI, the individual tumor markers that distinguish these non-MSI-high pathways have been less consistently associated with patient survival. However, recent evidence suggests that considering the joint combinations of these tumor markers may better inform prognosis for CRC patients and survivors.^2,6

Using data from 7 observational studies contributing to the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colon Cancer Family Registry (CCFR), we evaluated the association of 4 tumor markers (MSI, CIMP, BRAF and KRAS mutation status), alone and in combination, with survival in 5,010 CRC patients.

METHODS

Study Populations

We pooled data from the Cancer Prevention Study-II Nutrition cohort (CPS-II),^7,8 the German Darmkrebs: Chancen der Verhutung durch Screening Study (DACHS),^9,10 the Diet Activity and Lifestyle Study (DALS),¹¹ the Health Professionals Follow-up Study (HPFS),¹² the Melbourne Collaborative Cohort Study (MCCS),¹³ the Nurses’ Health Study (NHS),^14,15 and population-based sites from the Colon Cancer Family Registry (CCFR).¹⁶ These studies, including the CCFR, participate in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Of the included studies, CPS-II, HPFS, NHS, and MCCS used a prospective cohort design, with follow-up for incident cancer and survival. DACHS, DALS, and the CCFR are case-control studies that enrolled participants after CRC diagnosis, with prospective follow-up for survival.

Studies identified incident CRC either via self-report of diagnosis from study participants, with confirmation via adjudication of medical records (CPS-II, HPFS, NHS), via population-based cancer registries (CPS-II, DALS, MCCS, CCFR), regional hospitals (DACHS), or healthcare management organizations (DALS). Analyses were restricted to patients with CRC for whom complete data on tumor markers, survival, and stage were available (N=5,010). All participants provided informed consent. All studies were approved by their respective Institutional Review Boards.

Assessment of Outcomes

Study-specific protocols for assessing survival have been described previously.^7,9,11–18 Most studies (CCFR, CPS-II, DACHS, DALS, MCCS) ascertained vital status via linkage to state or national death registries, or state cancer registries, with cause of death verified by death certificates. Other studies used active follow-up to ascertain vital status (HPFS, NHS), with dates and causes of death confirmed via review of death certificates and/or medical records by trained adjudicators.

Tumor Marker Data

All studies extracted DNA from formalin-fixed paraffin-embedded tumor tissue specimens for tumor marker testing. Assays and protocols differed slightly across studies, with overlap as described below.

Microsatellite Instability (MSI) Status

Testing for MSI was performed via polymerase chain reaction (PCR) assays. Study-specific marker panels for PCR-based MSI assays are provided in Supplemental Table 1. Most panels were based on the Bethesda Consensus Panel¹⁹ (CCFR, CPS-II, HPFS, MCCS, NHS), with minor variability. For these studies, tumors were classified as MSI-high if ≥30% of the markers showed instability, and as non-MSI-high if 0 to 29% of the markers showed instability.

DACHS determined MSI status using a mononucleotide marker panel²⁰ that has high concordance with the Bethesda Consensus Panel for the detection of MSI-high status. MSI testing for DALS^21–23 used a panel of two mononucleotide and 10 tetranucleotide repeats that has also been shown to correlate highly with the Bethesda Panel for MSI-high detection.²⁴ Tumoral and normal DNA were PCR amplified with these 12 primer sets, and a classification of MSI for each marker was defined as ≥1 new PCR products either smaller or larger than those produced from normal DNA. Specifically, for BAT26, the PCR product from tumor had to be ≥4 base pairs smaller than that from germline DNA. A tumor classification of MSI from the tetranucleotide repeat panel was based on ≥30% markers showing instability and non-MSI-high if <30% of repeats were unstable.

BRAF and KRAS Somatic Mutation Status

The CCFR, CPS-II, and MCCS tested for BRAF c.1799T>A (p.V600E) mutations using a fluorescent allele-specific PCR assay.²⁵ DACHS used both Sanger sequencing and IHC analysis of p.V600E expression to determine BRAF mutation status;²⁶ for sequencing, they amplified BRAF exon 15 using FideliTaq polymerase and sequenced using the BigDye Terminator v1.1 Cycle Sequencing Kit on an ABI 3500 Genetic Analyzer. DALS evaluated BRAF mutations by amplifying exon 15 using Applied Biosystems AmpliTaq Gold and sequencing.²⁷ In HPFS and NHS, PCR and Pyrosequencing in BRAF exon 15 were used to identify hotspot mutations.^28,29

For KRAS, the CCFR and CPS-II used Sanger sequencing to assess mutations in KRAS codons 12 and 13.³⁰ DACHS determined KRAS mutation status by a single stranded conformational polymorphism technique or by Sanger sequencing.²⁶ DALS evaluated KRAS mutations by amplifying codons 12 and 13 using Taq FS DNA polymerase, and sequencing using prism BigDye terminators and cycle sequencing on an ABI prism 377 automated sequencer.³¹ Pyrosequencing was used in HPFS and NHS to detect mutations in KRAS codons 12 and 13,³² as well as codons 61 and 146.³³ MCCS used real-time PCR with high resolution melting analysis followed by direct Sanger sequencing for positive tumors to identify KRAS mutations in codons 12 and 13.³⁴

Here we classified tumors as having a BRAF mutation if the p.V600E mutation was identified, and as being KRAS-mutated if any mutation in codons 12 or 13 was identified.

CpG Island Methylator Phenotype (CIMP) Status

Studies conducted methylation analysis on different panels to determine CIMP status (Supplemental Table 2). The CCFR,^35,36 CPS-II, HPFS,^37–39 MCCS,⁴⁰ and NHS^37–39 used MethyLight⁴¹ to determine CIMP status. CPS-II, HPFS, and NHS used an eight-gene panel; CCFR and MCCS used a five-gene panel. DACHS determined CIMP status using a different five-gene panel⁴² and methods described by Warth et. al.⁴³ DALS determined CIMP status using a classic panel of CpG islands.^44–46 We created two CIMP categories for this analysis: CIMP-positive and non-CIMP. In instances where studies categorized tumors as CIMP-high, CIMP-low, and CIMP-negative, we combined CIMP-low and CIMP-high into the CIMP-positive category.

Subtype Classifications

Tumor subtypes were defined as follows, consistent with previously-suggested classifications (Supplemental Table 3):^1,4 1) Type 1 (i.e., MSI-high, CIMP-positive, BRAF-mutated, no pathogenic KRAS mutations); 2) Type 2 (i.e., MSS/MSI-low, CIMP-positive, BRAF-mutated, no pathogenic KRAS mutations); 3) Type 3 (i.e., MSS/MSI-low, non-CIMP, no pathogenic BRAF mutations, KRAS-mutated); 4) Type 4 (i.e., MSS/MSI-low, non-CIMP, no pathogenic BRAF or KRAS mutations); and 5) Type 5 (i.e., MSI-high, non-CIMP, no pathogenic BRAF or KRAS mutations). We grouped other marker combinations together as an additional category.

Statistical Analysis

We used Cox proportional hazards regression to evaluate differences in survival experience after diagnosis according to the aforementioned tumor marker combinations, and the individual tumor markers that comprise those combinations. The time axis was defined as days since diagnosis, with left truncation for studies that enrolled patients after CRC diagnosis. We conducted separate analyses for disease-specific and overall survival time (DSS and OS, respectively). Participants alive at their last vital status assessment were censored at that date. In analyses of DSS, persons who died from causes other than CRC were censored at the time of death. Proportional hazards assumptions were assessed by testing for a non-zero slope of the scaled Schoenfeld residuals on ranked failure times.⁴⁰ Our primary analyses used data pooled across all included studies, with adjustment for study population. We also conducted study-specific analyses for all exposure-outcome relationships, and present pooled estimates only where no significant evidence of heterogeneity across studies was observed. All analyses were adjusted for age at diagnosis and sex; we conducted separate analyses with and without adjustment for stage at diagnosis. We conducted secondary analyses stratified by the median age at diagnosis (<67 / ≥67 years), sex, and stage of disease at diagnosis (stage I-III / IV).

RESULTS

Study population characteristics are provided in Table 1. Although the distribution of individual tumor markers differed across studies, the prevalence of each tumor marker was consistent with previous estimates when combined across studies: 14.3% of tumors were MSI-high (study-specific range 7%−22%), 17.6% were CIMP-positive (range 9%−27%), 33.0% were KRAS-mutated (range 31%−52%), and 11.8% were BRAF-mutated (range 2%−19%).

Table 1.

Characteristics of included cases by study population^*

		Study Population^**
		CPS-II (N=488)	DACHS (N=1767)	DALS (N=580)	HPFS (N=43)	MCCS (N=252)	NHS (N=64)	CCFR (N=1816)
Age at diagnosis
<67		71 (15)	675 (38)	265 (45)	14 (33)	95 (38)	33 (52)	1310 (72)
≥67		417 (85)	1092 (62)	315 (54)	29 (67)	157 (62)	31 (48)	506 (28)
Mean (SD)		73.5 (6.5)	68.9 (10.8)	65.7 (9.5)	69.8 (7.9)	68.3 (8.2)	65.5 (7.0)	58.7 (10.4)
Stage at diagnosis
I		213 (44)	337 (19)	184 (32)	12 (28)	53 (21)	12 (19)	424 (23)
II-III		250 (51)	1193 (68)	340 (58)	25 (58)	154 (61)	41 (64)	1226 (68)
IV		25 (5)	237 (13)	56 (10)	6 (14)	45 (18)	11 (17)	166 (9)
Sex
Male		246 (50)	1023 (58)	318 (55)	43 (100)	117 (54)	0	941 (52)
Female		242 (50)	744 (42)	262 (45)	0	135 (46)	64 (100)	875 (48)
Tumor site
Proximal colon		235 (48)	651 (37)	298 (51)	19 (44)	94 (37)	31 (48)	641 (35)
Distal colon		143 (29)	468 (26)	273 (46)	12 (28)	68 (27)	18 (28)	492 (27)
Rectum		110 (23)	645 (37)	0	12 (28)	84 (33)	15 (23)	541 (30)
NOS		0	3 (0.2)	17 (3)	0	6 (2)	0	142 (8)
MSI status:	Non-MSI-high	406 (83)	1566 (89)	491 (85)	40 (93)	222 (88)	50 (78)	1521 (84)
MSI status:	MSI-high	82 (17)	201 (11)	89 (15)	3 (7)	30 (12)	14 (22)	295 (16)
CIMP status:	Non-CIMP	413 (85)	1447 (82)	423 (73)	39 (91)	223 (88)	49 (77)	1533 (84)
CIMP status:	CIMP-positive	75 (15)	320 (18)	157 (27)	4 (9)	29 (12)	15 (23)	283 (16)
KRAS status:	Wildtype	334 (68)	1192 (67)	386 (67)	21 (48)	173 (69)	36 (56)	1216 (67)
KRAS status:	Mutated	154 (32)	575 (33)	194 (33)	22 (52)	79 (31)	28 (44)	600 (33)
BRAF status:	Wildtype	412 (84)	1624 (92)	513 (88)	42 (98)	216 (86)	52 (81)	1558 (86)
BRAF status:	Mutated	76 (16)	143 (8)	67 (12)	1 (2)	36 (14)	12 (19)	258 (14)
Median survival (years)		7.7	3.2	5.0	7.1	8.9	8.4	12.0
(25%ile, 75%ile)		(4.8–10.8)	(2.3–5.0)	(3.5–6.8)	(3.5–10.6)	(5.9–12.0)	(3.8–10.8)	(5.7–15.6)

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Includes only cases with known survival status after CRC diagnosis and complete for stage and all four tumor markers

^**

CPS-II = Cancer Prevention Study II; DACHS = Darmkrebs: Chancen der Verhutung durch Screening Study; DALS = Diet Activity and Lifestyle Study; HPFS = Health Professionals Follow-Up Study; MCCS = Melbourne Collaborative Cohort Study; NHS = Nurses’ Health Study; CCFR = Colon Cancer Family Registry

In analyses of previously-specified tumor marker combinations, individuals with Type 2 tumors (MSS/MSI-low, CIMP-positive, BRAF-mutated, no pathogenic KRAS mutation) had poorer survival compared to those with Type 4 tumors (MSS/MSI-low, non-CIMP, BRAF- and no pathogenic KRAS mutation) [hazard ratio (HR) HR_DSS=1.66, 95% confidence interval (CI): 1.33–2.07; HR_OS=1.33, 95% CI: 1.11–1.59] (Tables 2 and 3). Those with Type 3 tumors (MSS/MSI-low, non-CIMP, no pathogenic BRAF mutation, KRAS-mutated) also experienced significantly poorer survival, although associations were more modest. Both MSI-high tumor subtypes (i.e., Types 1 and 5) were associated with more favorable survival relative to Type 4 for DSS (HR_type1=0.42, 95% CI: 0.27–0.64; HR_type5=0.70, 95% CI: 0.44–1.09), although associations were stronger for Type 1 and were not evident in analyses of OS.

TABLE 2.

Tumor marker characteristics and CRC disease-specific mortality

	N deaths / total	HR (95% CI)^†	p-value	HR (95% CI)^†§	p-value
Tumor subtype^¥
Type 1	23 / 295	0.35 (0.23–0.54)	1.5x10⁻⁶	0.42 (0.27–0.64)	5.6x10⁻⁵
Type 2	95 / 317	1.65 (1.33–2.06)	8.6x10⁻⁶	1.66 (1.33–2.07)	8.7x10⁻⁶
Type 3	343 / 1311	1.33 (1.15–1.52)	6.5x10⁻⁵	1.25 (1.09–1.44)	0.001
Type 4 (ref)	491 / 2366	1.0		1.0
Type 5	20 / 169	0.55 (0.35–0.85)	0.008	0.70 (0.44–1.09)	0.12
MSI status
Non-MSI-high	1010 / 4263	1.0 (ref)	2.0x10⁻¹⁶	1.0 (ref)	6.6x10⁻¹⁰
MSI-high	68 / 711	0.31 (0.24–0.41)		0.42 (0.32–0.55)
CIMP status
Non-CIMP	907 / 4095	1.0 (ref)	0.04	1.0 (ref)	0.10
CIMP-positive	171 / 879	1.23 (1.01–1.49)		1.17 (0.97–1.42)
BRAF-mutation status
BRAF-wildtype	968 / 4385	^ǂ		^ǂ
BRAF-mutated	110 / 589
KRAS-mutation status
KRAS-wildtype	666 / 3331	1.0 (ref)	4.2x10⁻⁴	1.0 (ref)	8.0x10⁻⁴
KRAS-mutated	412 / 1643	1.26 (1.11–1.43)		1.24 (1.09–1.41)

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^†

Adjusted for age, sex, and study population; analyses of individual tumor markers are adjusted for other markers.

^§

Adjusted also for stage at diagnosis.

^ǂ

Heterogeneity in study-specific estimates precluded pooled analysis.

^¥

Type 1 = MSI -high, CIMP-positive, BRAF-mutated, KRAS-wildtype; Type 2 = non-MSI-high, CIMP-positive, BRAF-mutated, KRAS-wildtype; Type 3 = non-MSI-high, non-CIMP, BRAF-wildtype, KRAS-mutated; Type 4 = non-MSI-high, non-CIMP, BRAF-wildtype, KRAS-wildtype; Type 5 = MSI-high, non-CIMP, BRAF-wildtype, KRAS-wildtype.

TABLE 3.

Tumor marker characteristics and overall mortality after CRC diagnosis

	N deaths / total	HR (95% CI)^†	p-value	HR (95% CI)^†§	p-value
Tumor subtype^¥
Type 1	97 / 296	0.78 (0.63–0.96)	0.02	0.85 (0.69–1.06)	0.15
Type 2	137 / 320	1.31 (1.09–1.57)	0.004	1.33 (1.11–1.59)	0.002
Type 3	542 / 1319	1.22 (1.10–1.36)	2.7x10⁻⁴	1.18 (1.06–1.31)	0.003
Type 4 (ref)	839 / 2387	1.0		1.0
Type 5	51 / 171	0.84 (0.63–1.11)	0.23	0.95 (0.72–1.27)	0.74
MSI status			8.8x10⁻⁷
Non-MSI-high	1633 / 4296	1.0 (ref)		1.0 (ref)	0.003
MSI-high	218 / 714	0.66 (0.56–0.78)		0.77 (0.65–0.92)
CIMP status
Non-CIMP	1522 / 4127	1.0 (ref)	0.10	1.0 (ref)	0.18
CIMP-positive	329 / 883	1.13 (0.98–1.32)		1.11 (0.95–1.29)
BRAF-mutation status
BRAF-wildtype	1617 / 4417	1.0 (ref)	0.21	1.0 (ref)	0.31
BRAF-mutated	234 / 593	1.12 (0.94–1.34)		1.10 (0.92–1.31)
KRAS-mutation status
KRAS-wildtype	1198 / 3358	1.0 (ref)	0.007	1.0 (ref)	0.007
KRAS-mutated	653 / 1652	1.15 (1.04–1.27)		1.14 (1.04–1.26)

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^†

Adjusted for age, sex, and study population; analyses of individual tumor markers are adjusted for other markers.

^§

Adjusted also for stage at diagnosis.

^¥

In stratified analyses, statistically significant associations with DSS for Types 1 and 2 were consistent regardless of stage, age at diagnosis, or sex (Table 4). The poorer DSS associated with Type 3 CRC was consistent across age strata, but was strongest among those diagnosed at stages I-III and among females. Greater variability was observed across strata in associations with OS.

Table 4.

Stratified associations of tumor subtypes with mortality after CRC diagnosis

Tumor Subtype^¥	Stage at diagnosis		Age at diagnosis		Sex
Tumor Subtype^¥	I-III	IV	<67	≥67	Male	Female
*Disease-Specific Mortality*
Type 1: HR (95% CI)	0.47 (0.30–0.73)^*	^ǂ	^ǂ	0.49 (0.30–0.78)^*	0.45 (0.20–1.02)	0.45 (0.27–0.74)^*
N deaths / total	21 / 291			19 / 211	6/81	17 / 214
Type 2: HR (95% CI)	1.68 (1.27–2.24)^*	1.77 (1.23–2.55)^*	1.80 (1.32–2.45)^*	1.50 (1.09–2.07)^*	1.56 (1.15–2.12)^*	1.76 (1.28–2.43)^*
N deaths / total	58 / 274	37/43	50 / 139	45 / 178	49 / 170	46 / 147
Type 3: HR (95% CI)	1.40 (1.17–1.68)^*	1.09 (0.87–1.36)	1.23 (1.02–1.49)^*	1.27 (1.04–1.56)^*	1.13 (0.94–1.37)	1.43 (1.16–1.76)^*
N deaths / total	215 / 1140	128 / 171	185 / 656	158 / 655	180 / 688	163 / 623
Type 4 (ref)	1.0	1.0	1.0	1.0	1.0	1.0
N deaths / total	290 / 2101	201 / 265	245 / 1199	246 / 1167	295 / 1405	196 / 961
Type 5: HR (95% CI)	0.71 (0.43–1.17)	^ǂ	0.68 (0.39–1.19)	0.71 (0.33–1.50)	0.80 (0.44–1.43)	0.59 (0.29–1.20)
N deaths / total	16 / 163		13 / 111	7 / 58	12/88	8/81
*Overall Mortality*
Type 1: HR (95% CI)	0.90 (0.72–1.12)	^ǂ	0.75 (0.47–1.21)	0.92 (0.72–1.18)	0.93 (0.63–1.36)	0.93 (0.71–1.21)
N deaths / total	95 / 292		18/84	79 / 212	28/82	69 / 214
Type 2: HR (95% CI)	1.18 (0.95–1.47)	1.84 (1.30–2.60)^*	1.61 (1.22–2.11)^*	1.11 (0.87–1.42)	1.24 (0.97–1.57)	1.40 (1.10–1.90)^*
N deaths / total	96 / 277	41/43	63 / 139	74 / 181	78 / 171	59 / 149
Type 3: HR (95% CI)	1.23 (1.08–1.39)^*	1.08 (0.87–1.34)	1.24 (1.05–1.46)^*	1.13 (0.98–1.31)	1.06 (0.92–1.22)	1.38 (1.17–1.63)^*
N deaths / total	403 / 1147	139 / 172	257 / 660	285 / 659	293 / 694	249 / 625
Type 4 (ref)	1.0	1.0	1.0	1.0	1.0	1.0
N deaths / total	620 / 2120	219 / 267	359 / 1212	480 / 1175	533 / 1419	306 / 968
Type 5: HR (95% CI)	0.98 (0.72–1.33)	^ǂ	0.80 (0.54–1.19)	1.14 (0.76–1.71)	0.93 (0.63–1.39)	1.00 (0.66–1.51)
N deaths / total	45 / 165		26 / 113	25/58	26/88	25/83

Open in a new tab

P<0.05

^ǂ

HR not estimated due to small numbers (<5 events)

^¥

Type 1 = MSI-high, CIMP-positive, BRAF-mutated, KRAS-wildtype; Type 2 = non-MSI-high, CIMP-positive, BRAF-mutated, KRAS-wildtype; Type 3 = non-MSI-high, non-CIMP, BRAF-wildtype, KRAS-mutated; Type 4 = non-MSI-high, non-CIMP, BRAF-wildtype, KRAS-wildtype; Type 5 = MSI-high, non-CIMP, BRAF-wildtype, KRAS-wildtype.

With regard to the individual tumor markers, the presence of MSI was associated with significantly better patient survival after diagnosis (HR_DSS=0.42, 95% CI: 0.32–0.55, HR_OS=0.77, 95% CI: 0.65–0.92) (Tables 2–3); this association was similar with and without adjustment for stage, and was consistent across strata defined by stage, age at diagnosis, and sex (Supplemental Table 4). In contrast, the presence of a KRAS somatic mutation was associated with significantly poorer patient survival (HR_DSS=1.24, 95% CI: 1.09–1.41, HR_OS=1.14, 95% CI: 1.04–1.26); this association was, again, impacted very little by adjustment for stage, and was consistent across evaluated strata. CIMP-positive status was modestly associated with poorer DSS and OS, although these associations were not statistically significant. In stratified analyses, associations with CIMP-positive status were consistent across stage and sex strata, with a slightly higher HR estimate for individuals diagnosed with CRC at ages <67 years (HR=1.38, 95% CI 1.04–1.81) (Supplemental Table 4).

In analyses comparing the survival experience of those with vs. those without a somatic V600E BRAF mutation, there was evidence of significant heterogeneity across included studies (p-heterogeneity=0.0021). Study-specific HR estimates for DSS ranged from a statistically significant positive association in DACHS (HR=2.31, 95% CI: 1.54–3.45) to a non-significant inverse association in CPS-II (HR=0.65, 95% CI: 0.22–1.89). Stratified analyses suggested that the association between BRAF mutation status and patient survival also differed by stage and age at diagnosis: BRAF mutation was not associated with DSS among those diagnosed with stage I-III CRC or diagnosed at ages <67, but was associated with significantly poorer survival in those diagnosed at stage IV or at ages ≥67 years (p-heterogeneity=0.18, Supplemental Table 4).

DISCUSSION

In this large pooled analysis of survival in patients with CRC, we observed significant differences in outcomes according to established tumor markers – when considered in pre-specified combinations and individually. Patients with Type 2 CRC had the poorest prognosis, particularly with respect to DSS. Regardless of other tumor markers, patients with MSI-high colorectal tumors had a favorable prognosis; however, this survival benefit was most evident when combined with CIMP-positive and BRAF-mutated status (i.e., Type 1 CRC). We also observed poorer patient survival associated with KRAS-mutated status in colorectal tumors, particularly in combination with MSS/MSI-low status (i.e., Type 3 CRC).

This study is the largest to date to investigate the survival implications for patients with tumors exhibiting these suggested tumor marker combinations, which reflect distinct proposed etiologies.¹ In particular, the tumor marker combinations of Type 1 and 2 subtypes have been suggested to reflect a serrated pathway, having origins in serrated polyps. In contrast, the molecular phenotype of the predominant Type 4 subtype was proposed by Jass¹ to reflect colorectal tumors arising as a result of the traditional adenoma-carcinoma sequence. Under Jass’s classification, colorectal tumors classified into our Type 3 subtype were suggested to arise via an alternative serrated pathway involving KRAS-mutated adenomas, whereas the phenotype of Type 5 tumors was suggested by Jass to be most indicative of familial disease. Although the precise origins of the evaluated tumors cannot be deciphered from the present analyses, our results support the prognostic heterogeneity of these diverse tumor types.

Our findings are consistent with two previous studies in which we evaluated comparable tumor marker combinations, in both the observational study setting² and in the context of clinical treatment trials.⁴⁷ In the Seattle Colon Cancer Family Registry, a study population partially overlapping with the CCFR participants included in this analysis, we observed an HR=2.20 (95% CI: 1.47–3.31) comparing DSS among patients with Type 2 vs. 4 tumors; we also similarly observed poorer survival for Type 3 vs. 4 CRC patients, and significantly better survival for patients with MSI-high subtypes.² In the clinical trial setting, we further observed that patients with stage III colon cancer whose tumors were MSS/MSI-low and BRAF-mutated had the poorest survival, regardless of treatment received.⁴⁷ Other studies have similarly noted poorer survival in patients with MSS/MSI-low colorectal tumors that are BRAF-mutated,^6,48–51 consistent with our Type 2 subtype. Adding to these findings, here we observed that the association of Type 2 CRC with poorer patient DSS remained statistically significant when stratified by stage, age at diagnosis, and sex.

Numerous other studies have examined the independent associations of MSI,^3,52,53 CIMP,^26,53–55 KRAS mutation,^31,56–59 and BRAF mutation^{52–54,57,58,60} status with CRC outcomes. Consistent with our results, MSI-high status has consistently been associated with favorable survival: results from a large meta-analysis indicated 40% better survival in patients with MSI-high vs. MSS/MSI-low tumors.³ Although this association is slightly stronger than what we observed after adjusting for stage and other tumor markers, we found that the favorable association with MSI-high status persisted across categories defined by stage, age, and sex. Studies of survival in patients with CRC in relation to CIMP status have, in contrast, been inconsistent.^53,54,61 We noted no evidence of association with CIMP after adjusting for stage and other tumor markers; however, the lack of an independent association with CIMP status does not rule out the importance of this marker as part of a more inclusive phenotype. Our observed associations with KRAS mutation status are also consistent with previous studies, generally indicating a poorer prognosis among those with KRAS-mutated vs. no pathogenic KRAS mutation CRC. We were not able to confirm the findings of numerous previous studies regarding the poorer prognosis associated with BRAF mutation status. However, our findings regarding the poor prognosis of Type 2 vs. 4 CRC implicates a prognostic role of BRAF mutation status when considered in combination with other markers.

Going beyond the evaluated tumor markers, Guinney et al. have suggested the existence of four consensus molecular subtypes (CMS) based primarily on patterns of gene-expression,⁶² with likely implications for survival in patients with CRC.^62–64 CMS classifications do not directly overlap with the tumor-marker based classifications used in the present analysis, although there is evidence of some similarity between CMS1 and Type 1 tumors (i.e., MSI-high, CIMP-positive, BRAF-mutated), and between CMS3 and Type 3 tumors (i.e., KRAS-mutated, CIMP-low / non-CIMP); however, even for these somewhat similar subtypes, survival patterns using CMS classifications^62–64 are distinct from those described here and in other studies of tumor marker-based classifications.^2,47,65 Although the evaluation of gene-expression patterns was beyond the scope of this analysis, and is often not an option in clinical settings, further study should consider the joint role of gene-expression and tumor marker characteristics in survival for patients with CRC.

The results of this investigation should be interpreted in the context of its limitations. A lack of detailed information regarding CRC treatment prevented us from evaluating the extent to which CRC subtypes differ with respect to treatment response. However, testing for the evaluated tumor markers was not clinically indicated during the time period in which the included patients were diagnosed; thus, knowledge of these markers would not have been used to make treatment decisions. Differential response to 5-fluorouracil-based chemotherapy⁶⁶ and checkpoint inhibitors⁶⁷ by MSI status, and differential response to anti-EGFR therapies by KRAS and BRAF mutation status⁵⁷ has been noted in previous studies. Although treatment with checkpoint inhibitors or anti-EGFR therapies would not have been available to the vast majority of patients included here, based on the range of diagnosis years, future studies should consider the contribution of different treatments to observed survival differences, and further elaborate on the clinical utility of molecular classifications.⁶⁵ Additionally, the present analysis was restricted to those study participants for whom complete tumor marker data were available. Participants for whom data were missing on 1–3 tumor markers were, on average, slightly older and more likely to have been diagnosed with stage I disease as compared to those with complete tumor marker data, but were similar with respect to sex and 5-year survival (Supplemental Table 5). Based on the limited tumor marker data we observed in this excluded case group, there was no evidence of differences in MSI or KRAS mutation status, but there was a suggestion of lower CIMP-positive and BRAF-mutated representation in those excluded vs. those included. Lastly, as previously described, tumor marker testing strategies differed slightly across studies. Thus, although we have pooled data across studies, there is the potential for some heterogeneity in tumor marker classifications. Although our analyses stratified by study population were limited by small numbers, we observed evidence of heterogeneity in study-specific findings only with respect to BRAF mutation status.

This study also has several important strengths. In particular, the large overall sample size allowed us to evaluate survival in patients with CRC subtypes defined by less common tumor marker combinations. The two smallest subtypes evaluated in the present analysis (i.e., Types 2 and 5) demonstrated the most pronounced differences in survival. Our large sample size also afforded us the opportunity to examine associations stratified by stage, age at diagnosis, and sex.

Taken together, the results presented here highlight the clinical importance of molecular heterogeneity in CRC. Current clinical practice includes testing of MSI status, KRAS mutation status, and/or BRAF mutation status only in a subset of patients with CRC – based on reported family history and stage at diagnosis. In evaluating CRC subtype classifications defined by proposed etiologic pathways, our findings demonstrate that these individual markers, in addition to CIMP status, are informative of CRC survival – above and beyond any relationship with stage at diagnosis – particularly when considered in combination.

Supplementary Material

NIHMS1568740-supplement-1.pdf^{(42.9KB, pdf)}

Acknowledgements:

CPS-II Nutrition Cohort: The authors thank the CPS-II participants and Study Management Group for their invaluable contributions to this research. The authors would also like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries, and cancer registries supported by the National Cancer Institute Surveillance Epidemiology and End Results program.

DACHS: We thank all participants and cooperating clinicians, and Ute Handte-Daub, Ansgar Brandhorst, Utz Benscheid, Muhabbet Celik and Ursula Eilber for excellent technical assistance. Harvard cohorts: The study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. We would like to thank the participants and staff of the HPFS and NHS for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.

Grant support:

Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO): National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services (U01 CA137088 and R01 CA176272). This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA015704.

The Colon Cancer Family Registry (CCFR) Illumina GWAS was supported by funding from the National Cancer Institute, National Institutes of Health (grant numbers U01 CA122839, R01 CA143247 to Dr. Graham Casey). The Colon CFR participant recruitment and collection of data and biospecimens used in this study were supported by the National Cancer Institute, National Institutes of Health (grant number U01 CA167551). Additional funding towards molecular characterization and analyses included the following grants from the National Cancer Institute, National Institutes of Health: K05CA152715 (to P.A.N.) and K07CA172298 (to A.I.P.). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Colon Cancer Family Registry (CCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government, any cancer registry, or the CCFR.

CPS-II: The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II (CPS-II) cohort. This study was conducted with Institutional Review Board approval.

DACHS: This work was supported by the German Research Council (BR 1704/6–1, BR 1704/6–3, BR 1704/6–4, CH 117/1–1, HO 5117/2–1, HE 5998/2–1, KL 2354/3–1, RO 2270/8–1 and BR 1704/17–1), the Interdisciplinary Research Program of the National Center for Tumor Diseases (NCT), Germany, and the German Federal Ministry of Education and Research (01KH0404, 01ER0814, 01ER0815, 01ER1505A and 01ER1505B).

DALS: National Institutes of Health (R01 CA48998 to M. L. Slattery).

HPFS is supported by the National Institutes of Health (P01 CA055075, UM1 CA167552, U01 CA167552, R01 CA137178, R01 CA151993, R35CA197735, K07 CA190673, and P50 CA127003), NHS by the National Institutes of Health (R01 CA137178, P01 CA087969, UM1 CA186107, R01 CA151993, R35 CA197735, K07CA190673, and P50 CA127003).

MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057, 396414 and 1074383, and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database.

OFCCR: National Institutes of Health, through funding allocated to the Ontario Registry for Studies of Familial Colorectal Cancer (U01 CA074783); see CCFR section above. Additional funding toward genetic analyses of OFCCR includes the Ontario Research Fund, the Canadian Institutes of Health Research, and the Ontario Institute for Cancer Research, through generous support from the Ontario Ministry of Research and Innovation.

Dr. Chan is a Stuart and Suzanne Steele MGH Research Scholar.

Abbreviations:

CCFR: Colon Cancer Family Registry
CI: confidence interval
CIMP: CpG island methylator phenotype
CPS-II: Cancer Prevention Study II nutrition cohort
CRC: colorectal cancer
DACHS: Darmkrebs: Chancen der Verhutung durch Screening Study
DALS: Diet Activity and Lifestyle Study
DSS: disease-specific survival
GECCO: Genetics and Epidemiology of Colorectal Cancer Consortium
HPFS: Health Professionals Follow-up Study
HR: hazard ratio
MCCS: Melbourne Collaborative Cohort Study
MSI: microsatellite instability
N/A: not applicable
NHS: Nurses’ Health Study
OFCCR: Ontario Familial Colorectal Cancer Registry
OS: overall survival
PCR: polymerase chain reaction

Footnotes

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Disclosures: None

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PERMALINK

Association Between Molecular Subtypes of Colorectal Tumors and Patient Survival, Based on Pooled Analysis of 7 International Studies

Amanda I Phipps

Elizabeth Alwers

Tabitha Harrison

Barbara Banbury

Hermann Brenner

Peter T Campbell

Jenny Chang-Claude

Daniel Buchanan

Andrew T Chan

Alton B Farris

Jane C Figueiredo

Steven Gallinger

Graham G Giles

Mark Jenkins

Roger L Milne

Polly A Newcomb

Martha L Slattery

Mingyang Song

Shuji Ogino

Syed H Zaidi

Michael Hoffmeister

Ulrike Peters

Abstract

Background and Aims:

Methods:

Results:

Conclusions:

BACKGROUND AND CONTEXT:

NEW FINDINGS:

LIMITATIONS:

IMPACT:

Lay Summary:

INTRODUCTION

METHODS

Study Populations

Assessment of Outcomes

Tumor Marker Data

Microsatellite Instability (MSI) Status

BRAF and KRAS Somatic Mutation Status

CpG Island Methylator Phenotype (CIMP) Status

Subtype Classifications

Statistical Analysis

RESULTS

Table 1.

TABLE 2.

TABLE 3.

Table 4.

DISCUSSION

Supplementary Material

Acknowledgements:

Abbreviations:

Footnotes

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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