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www.nature.com/npjbcancer REVIEW ARTICLE OPEN TGFBR1*6A as a modiﬁer of breast cancer risk and progression: advances and future prospects 1 1 1 1 1 1 Kojo Agyemang , Allan M. Johansen , Grayson W. Barker , Michael J. Pennison , Kimberly Shefﬁeld , Hugo Jimenez , 1 1 2 1,3 1,4 1,3 1,3,5 Carl Blackman , Sambad Sharma , Patrick A. Fordjour , Ravi Singh , Katherine L. Cook , Hui-Kuan Lin , Wei Zhang , 1,3 1,3 1,3 3,6,7 1,3 Hui-Wen Lo , Kounosuke Watabe , Peiqing Sun , Carl D. Langefeld and Boris Pasche There is growing evidence that germline mutations in certain genes inﬂuence cancer susceptibility, tumor evolution, as well as clinical outcomes. Identiﬁcation of a disease-causing genetic variant enables testing and diagnosis of at-risk individuals. For breast cancer, several genes such as BRCA1, BRCA2, PALB2, ATM, and CHEK2 act as high- to moderate-penetrance cancer susceptibility genes. Genotyping of these genes informs genetic risk assessment and counseling, as well as treatment and management decisions in the case of high-penetrance genes. TGFBR1*6A (rs11466445) is a common variant of the TGF-β receptor type I (TGFBR1) that has a global minor allelic frequency (MAF) of 0.051 according to the 1000 Genomes Project Consortium. It is emerging as a high frequency, low penetrance tumor susceptibility allele associated with increased cancer risk among several cancer types. The TGFBR1*6A allele has been associated with increased breast cancer risk in women, OR1.15 (95% CI 1.01–1.31). Functionally, TGFBR1*6A promotes breast cancer cell proliferation, migration, and invasion through the regulation of the ERK pathway and Rho- GTP activation. This review discusses current ﬁndings on the genetic, functional, and mechanistic associations between TGFBR1*6A and breast cancer risk and proposes future directions as it relates to genetic association studies and mechanisms of action for tumor growth, metastasis, and immune suppression. npj Breast Cancer (2022) 8:84 ; https://doi.org/10.1038/s41523-022-00446-6 HERITABLE BREAST CANCER GENES rationale to assess its role as a modiﬁer gene for breast cancer predisposition and tumor progression. Heritable predisposition genes are important risk factors for breast cancer susceptibility, accounting for 5.03% of all breast 1–4 cancer cases . Pathogenic variants of high-risk predisposition TGFBR1*6A AND BREAST CANCER genes such as BRCA1 and BCRA2 are the most widely known and are used in genetic testing and counseling to predict breast Identiﬁcation and characterization 4,5 cancer risk and clinical outcomes . However, these variants are The TGF-β pathway functions as a tumor suppressor during cancer uncommon (<1.3% allelic frequency), and account for less than development but enhances tumor growth, immune evasion, and 4,6 15–17 1.5% of all breast cancer cases . Like other complex genetic metastasis in advanced cancers . During TGF-β signaling, the traits such as diabetes, obesity, and autoimmune diseases, recent TGF-β receptor 1 (TGFBR1) plays a critical role of binding and advances in genome-wide association studies reveal that the vast activating downstream receptor-regulated (R-) SMADs (SMAD2 19,20 majority of hereditary breast cancer cases are genetically multi- and SMAD3), co-SMADs (SMAD4) , and non-SMAD (MAPK-ERK, 7–10 17,21 factorial , involving numerous other polymorphisms of varying RAS, AKT, JNK, and RHOA) intermediary proteins. In 1998, 11–13 penetrance acting as tumor modiﬁer genes . Thus, several Pasche et al. identiﬁed TGFBR1*6A (rs11466445) as a polymorphic lower penetrance risk variants are now included in breast cancer variant of TGFBR1 with an in-frame deletion of three GCG codons susceptibility gene screening panels for testing and counseling. encoding alanine within exon 1 of the human TGFBR1 signal 22,23 Inclusion of these polymorphisms into screening panels results in peptide sequence (Fig. 1). Signal peptides are responsible for a 40 to 50% increase in breast cancer risk detection among intracellular transport, targeting of nascent proteins (secretory and women, and a 5 to 15% increase in detection among BRCA1/2- membrane proteins) to the endoplasmic reticulum, and integra- 6,11,12,14 negative females . tion of newly translated proteins into their respective compart- The Transforming Growth Factor-Beta (TGF-β) signaling path- ments. N-terminal signal peptides are usually cleaved off and way plays a critical role during cancer development and degraded after insertion and targeting of the nascent protein to 15–17 progression . Variants of the TGF-β pathway genes, particu- the endoplasmic reticulum . Investigations into TGFBR1*6A signal larly TGFBR1*6A, have been studied in a large number of females, peptide cleavage showed that the 3-alanine deletion from the and have been associated with low penetrance risk for breast 9-alanine repeat within the hydrophobic core of the signal peptide cancer (Table 1). This review details our current clinical and pre- does not affect the posttranslational cleavage of a signal peptide. clinical knowledge on TGFBR1*6A as a high frequency, low Studies showed that TGFBR1*6A signal peptide is cleaved between penetrance tumor susceptibility allele, and provides further Ala30 and Leu31, whereas the wild-type TGFBR1 is cleaved 1 2 Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157-1082, USA. Department of Thoracic Medicine, School of Medicine, University of 3 4 Adelaide, Adelaide, SA 5000, Australia. Comprehensive Cancer Center, Wake Forest School of Medicine, Winston Salem, NC 27157-1082, USA. Department of Surgery, Wake Forest School of Medicine, Winston-Salem, NC 27157-1082, USA. Center for Cancer Genomics and Precision Oncology, Wake Forest School of Medicine, Winston Salem, NC 6 7 27157-1082, USA. Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC 27157-1082, USA. Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157-1082, USA. email: firstname.lastname@example.org Published in partnership with the Breast Cancer Research Foundation 1234567890():,; K. Agyemang et al. Table 1. Case-control studies showing TGFBR1*6A genotypic and allelic distribution and frequency. Study Country of study Self-reported Genotype distribution and frequency (%) Allelic frequency participants race/ethnicity Cases Controls Cases Controls 9/9A 9A/6A 6A/6A 9A/9A 9A/6A 6A/6A TGFBR1*6A TGFBR1 Pasche et al. 1999 (a) US Mixed 128 (84.2) 24 (15.8) 0 (0) 654 (89.3) 78 (10.7) 0 (0) 0.079 0.053 Pasche et al. 1999 (b) Northern Italy Caucasian 39 (81.3) 8 (16.7) 1 (2.1) 38 (76) 12 (24) 0 (0) 0.104 0.120 Baxter et al. 2002 United Kingdom Caucasian 268 (75.5) 83 (23.4) 4 (1.1) 207 (83.5) 39 (15.7) 2 (0.8) 0.128 0.087 Reiss, 2004 US Mixed 87 (88.8) 11 (11.2) 0 (0) 77 (84.6) 14 (15.4) 0 (0) 0.056 0.077 Caldes, 2004 Spain Caucasian 214 (79) 56 (20.7) 1 (0.4) 250 (85.6) 42 (14.4) 0 (0) 0.107 0.072 Ofﬁt, 2004 US NS 391 (84.6) 67 (14.5) 4 (0.9) 291 (88.2) 38 (11.5) 1 (0.3) 0.081 0.061 Northwestern, 2004 US NS 74 (86.1) 12 (13.9) 0 105 (85.4) 17 (13.8) 1 (0.8) 0.070 0.077 Jin et al. 2004 (a) Finland Caucasian 177 (80.1) 38 (17.2) 6 (2.7) 171 (73.1) 60 (25.6) 3 (1.3) 0.113 0.141 Jin et al. 2004 (b) Poland Caucasian 140 (82.4) 28 (16.5) 2 (1.2) 176 (87.1) 26 (12.9) 0 (0) 0.094 0.064 Kaklamani et al. 2005 US Mixed 515 (84.3) 92 (15.1) 4 (0.7) 612 (88.7) 77 (11.2) 1 (0.1) 0.082 0.057 Chen et al. 2006 US Mixed 92 (80) 23 (20) 0 (0) 111 (85.4) 18 (13.8) 1 (0.8) 0.100 0.077 # a a Feigelson et al. 2006 US Mixed 387 (80.5) 94 (19.5) NS 384 (74) 100 (26) NS 0.098 0.130 Cox et al. 2007 US NS 968 (81.6) 207 (17.4) 12 (1) 1352 (80.8) 302 (18.1) 19 (1.1) 0.097 0.102 Song et al. 2007 Sweden Caucasian 598 (78.4) 152 (19.9) 13 (1.7) 682 (80) 160 (18.8) 10 (1.2) 0.117 0.106 Jakubowska et al. 2009 Poland Caucasian 282 (88.7) 33 (10.4) 3 (0.9) 252 (86.9) 38 (13.1) 0 (0) 0.061 0.066 Colleran et al. 2009 Ireland Caucasian 796 (82.9) 154 (16) 10 (1) 785 (81.9) 160 (16.7) 13 (1.4) 0.091 0.097 Joshi 2011 (a) India Asian 163 (97.6) 4 (2.4) 0 (0) 213 (95.9) 9 (4.1) 0 (0) 0.012 0.020 Joshi 2011 (b) India Asian 33 (78.6) 8 (19) 1 (2.4) 148 (87.6) 19 (11.2) 2 (1.2) 0.119 0.068 Kamali et al. 2015 Iran Middle 251 (89.6) 25 (8.9) 4 (1.4) 241 (86.1) 27 (9.6) 12 (4.3) 0.059 0.091 Eastern Case-control studies from 1999 to date have included a total of 14,837 participants (6787 cases /8050 controls); of which 13,312 (6026 cases/7286 controls) were in Hardy–Weinberg (HW) equilibrium. The data shows genotype distribution and allelic frequency (%) in the order TGFBR1*9A/9A > TGFBR1*9A/ 6A > TGFBR1*6A/6A. NS not stated. Study population not in Hardy–Weinberg equilibrium. GENETIC ASSOCIATION WITH BREAST CANCER RISK Case-control studies Allelic frequency. TGFBR1*6A (rs11466445) is a common variant of the TGF-β receptor type I (TGFBR1) with a study-wide minor allelic frequency (MAF) of 0.051 in the 1000 Genomes Project Consortium, ranging from 0.0079 among East Asians to 0.0975 29–31 among Europeans . Nineteen case-control studies that included 14,837 participants (6787 cases/8,050 controls) have investigated the association of TGFBR1*6A with breast cancer risk (Table 1). Seventeen of these studies, which included 13,312 individuals (6026 cases/7286 controls), show the TGFBR1*6A Fig. 1 TGFBR1 and TGFBR1*6A gene and protein sequences. genotype frequencies were in Hardy–Weinberg equilibrium Sequence analyses reveal nine (9) GCG/alanine repeats within (HWE). HWE is a useful genotyping quality control metric that nucleotides 42–119 of the TGFBR1 signal sequence. TGFBR1*6A relates allele frequencies to genotype frequencies with an variant has six (6) GCG/alanine repeats in its signal sequence expected assumption that genotype frequencies will remain (Pasche, Luo et al. 1998, Pasche, Kolachana et al. 1999). constant in a randomly mating population. The afore-mentioned TGFBR1*6A case/control studies consisted predominantly of Caucasian individuals of European ancestry (>80%), but also between Ala33 and Leu34. Importantly, the mature forms of the included modest numbers of individuals from other self-reported TGFBR1*6A and TGFBR1 receptors are identical and the TGF-β 23,25 races/ethnicities (e.g., African American, Hispanic, Asian, and ligand binds to each receptor with the same afﬁnity . Also, the Middle Eastern). Early studies examined a mixed sample of differences in cleavage sites do not inﬂuence TGFBR1*6A protein Caucasians, African Americans, Hispanics, and Asians from the US, targeting and translocation functions nor its sensitivity, and half- and Northern Italy, and found an allelic frequency of 7.9% in life . Despite TGFBR1*6A and TGFBR1 physicochemical simila- 23 breast cancer patients compared to 5.3% in healthy controls . rities, TGFBR1*6A is intriguingly associated with breast cancer Several other studies from the US consisting of >80% Caucasians 26,27 risk , and promotion of cell growth, migration, and inva- and <20% non-Caucasians reported similar TGFBR1*6A allelic 25,28 26,32–35 sion . Only the released signal peptide separates TGFBR1*6A frequency of 5.6–10.0% among breast cancer patients . The from TGFBR1, which strongly suggests that TGFBR1*6A signal TGFBR1*6A allelic frequency also ranged from 6.1 to 12.8% among peptide contributes to oncogenesis. breast cancer patients from northern and southwestern Europe, npj Breast Cancer (2022) 84 Published in partnership with the Breast Cancer Research Foundation 1234567890():,; K. Agyemang et al. 36 32 37 38 including samples from the UK , Spain , Sweden , Finland , in very low a priori statistical power to detect an association of the 38,39 40 Poland , and Ireland (Table 1). In all, Asian and Middle Eastern size previously reported. To date, TGFBR1*6A appears to be women, particularly those from Western India had the lowest associated with decreased breast cancer risk only in Middle 41 42 TGFBR1*6A allelic frequency, 1.2–11.9% among cases . In all, Eastern women, particularly in Iran (OR 0.62, 95% CI 0.39–0.98) . TGFBR1*6A genotype frequency is in the order 9A/9A > 9A/ However, it is worth noting that the genotype frequencies in this 23,38,41 6A > 6A/6A in all populations studied to date (Table 1) . sample of 280 cases and 280 controls show strong deviations from Hardy–Weinberg equilibrium expectations (X p value = 2.81E-12), Risk association. Published case-control studies examining the an important measure of genotyping quality. Speciﬁcally, there association of TGFBR1*6A with breast cancer risk among were an excess of both TGFBR1*6A and TGFBR1 homozygotes, individuals from different geographical locations and ethnicities with a corresponding deﬁcit of heterozygotes, when compared to 26,41,42 show both signiﬁcant and non-signiﬁcant risk associations . HWE expectations. It remains unclear if the differences in TGFBR1*6A risk association In summary, it appears that TGFBR1*6A is a common breast correlate with geographical location, ethnicity, age, tumor stage, cancer susceptibility allele in diverse ethnic backgrounds and its and other confounding factors such as other polymorphism inﬂuence may vary by ancestry, geographical location, and other frequencies, lifestyle, and environment. In 2002, Baxter et al. risk modifying factors. Large case/control studies, with important reported the ﬁrst association between TGFBR1*6A and breast related phenotypes, paralleling the magnitude of other complex cancer risk (OR 1.6, 95% CI 1.1–2.5) . Cases and controls were genetic traits, and meta-analyses that incorporate strict genotype residents of Southampton, UK. Controls were healthy females. quality control assessment will be necessary to clarify its Breast cancer cases were selected based on age at onset under 40 population-speciﬁc subgroup risk associations. years, family history of breast cancer irrespective of age at onset, or bilateral breast cancer irrespective of family history or age at META-ANALYSES OF TGFBR1*6A ASSOCIATION WITH BREAST onset. The study noted that TGFBR1*6A allelic frequency among CANCER RISK Caucasians from the UK does not differ by age of onset (<40 years), bilateral breast cancer, family history, and germline To derive a more precise estimate of TGFBR1*6A association with mutations of BRCA1 and BRCA2 . In 2004, Caldes investigated breast cancer risk, several meta-analyses have investigated its risk breast cancer patients of Caucasian descent only from Madrid, association in up to 14,837 participants (6787 cases/8050 controls) Spain. The study found a signiﬁcant association between from 19 case-control studies. In all, eight meta-analyses have TGFBR1*6A and breast cancer risk (OR 1.55, 95% CI 1.02–2.34) . studied the association of TGFBR1*6A with breast cancer risk In the Kaklamani 2005 study that included >80% Caucasians (Fig. 2b). Five of the studies found evidence that supports the from New York, NY, TGFBR1*6A association with breast cancer risk association between TGFBR1*6A and breast cancer risk. Early 44 32 was signiﬁcant under both dominant (OR 1.50, 95% CI 1.07-2.11) meta-analyses by ref. and ref. that analyzed up to 12 case- and additive (OR 1.46, 95% CI 1.04-2.06) models. Breast cancer risk control studies (4871 subjects) found an association of TGFBR1*6A for women aged >50 years was higher (OR 2.20, 95% CI 1.25–3.87), with breast cancer risk with odds ratios of 1.48 (95% CI 1.11–1.96) than for women aged ≤50 years (OR 1.18, 95% CI 0.75–1.84). and 1.38 (95% CI 1.14–1.67), respectively. Subsequent studies However, further subgroup analyses show no association between examined ten case/control studies and did not ﬁnd a signiﬁcant TGFBR1*6A and ER/PR status or cancer stage at diagnosis. In this risk association (OR 1.10, 95% CI 0.89–1.38), albeit in a concordant study, both breast cancer patients and healthy controls were direction. The heterogeneity in the reported results may be partly matched for age, gender, and location . Other studies that due to meaningful differences in the genotyping methods included participants of similarly mixed ethnicities but from other employed across these studies for this GCG repeat polymorphism, states in the United States did not conﬁrm an association between and increased variation in estimates due to lower sample sizes 32,33,35 35 TGFBR1*6A and breast cancer risk (Fig. 2a). This indicates included in the case/control studies . that TGFBR1*6A association with breast cancer risk may have a Three of the most recent meta-analyses that investigated up to more modest effect size than in the original reports (regression 17 case/control studies (14,068 subjects) found an association toward the mean) and these studies were not powered to account between TGFBR1*6A and breast cancer risk. Liao et al. 2010 for the effects of other confounding/modifying factors apart from reported an odds ratio of 1.16 (95% CI 1.01–1.34) among 11,220 ethnicity and geographical location that modify the magnitude of case/controls . Wang et al. 2012 investigated 14,068 participants the risk attributable to the TGFBR1*6A polymorphism. and showed a nearly identical OR of 1.15 (95% CI 1.01–1.31) . Intriguingly, other studies have investigated participants of Additionally, Ou et al. 2015 found 14.8 and 9.6% TGFBR1*6A allelic similar Caucasian backgrounds from other European countries distribution among 6275 cases and controls, respectively. There (Sweden, Ireland, Finland, and Poland) and found no signiﬁcant was a signiﬁcant association of the TGFBR1*6A allele with breast 37–40 46 associations (Fig. 2a) . In a case-control study among Swedish cancer risk (OR 1.33 95% CI 1.02–1.73) . The study also noted that Caucasians , the study further enriched its population sampling the homozygous TGFBR1*6A/6A is not signiﬁcantly associated with 40 47 for genetic susceptibility by recruiting patients with family history breast cancer risk. Colleran 2010 and Krishna 2020 found no of bilateral breast cancer cases. Overall, TGFBR1*6A carriership was signiﬁcant associations between TGFBR1*6A and breast cancer risk not associated with breast cancer risk (OR 1.12, 95% CI 0.90–1.39). (Fig. 2b). It is interesting to note that the latter two meta-analyses However, subgroup analyses showed that TGFBR1*6A was selectively excluded some data from the Pasche 2004, and other associated with increased risk among low-risk familial breast case/control studies in their analyses. For example, case/control 32,39 cancer patients with one ﬁrst- or second-degree breast cancer studies by refs. , all of which are in Hardy–Weinberg 35 40 relative (OR 1.3, 95% CI 1.0–1.9). In that study, TGFBR1*6A also equilibrium were selectively omitted from the ref. , ref. and correlated with higher tumor grade (OR 2.27 (95% CI 1.01–5.11) ref. meta-analyses. Colleran et al., 2010 explained that some but had no association with tumor stage or ER/PR status . For study populations from Jin et al., Reiss, and Ofﬁt had been women of Asian and Middle Eastern descent, TGFBR1*6A reported in Kaklamani et al. 2005, and as such omitted them in association with breast cancer risk was neither signiﬁcant nor their analyses. However, these study populations were different 41,42 41 protective . Among Asians from India, Joshi 2011 found no and there was no overlap with the population reported in association among Western Indians (OR 0.69, 95% CI 0.18–1.92) Kaklamani et al. 2005 and prior reports. Additionally, Colleran and those from the Parsi community (OR 1.85, 95% CI 0.84–4.05) . et al., measured heterogeneity in the case/control studies used in Although these are important studies, the sample sizes are their meta-analyses, and, noted that studies with sample size less modest, and the lower allelic frequency in this population results than 1000 had the most extreme odds ratios, indicating odds ratio Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 84 K. Agyemang et al. Case-control studies Number of subjects Odds ratio Case Control Author (Year) CI=95% Pasche et al. 1999 (a) 152 732 1.52 (0.96-2.46) Pasche et al. 1999 (b) 48 50 0.85 (0.36-2.08) Baxter et al. 2002 355 248 1.60 (1.10-2.5) Reiss, 2004 98 91 0.71 (0.32-1.61) Caldes, 2004 271 292 1.55 (1.02-2.34) Offit, 2004 462 330 1.37 (0.92-2.04) Northwestern, 2004 86 123 0.94 (0.42-1.90) Jin et al. 2004 (a) 221 234 0.78(0.52-1.15) Jin et al. 2004 (b) 170 202 1.10 (0.81-1.49) Kaklamani et al. 2005 611 690 1.46 (1.04-2.06) Chen et al. 2006 115 130 1.33 (0.71-2.60) Cox et al. 2007 1187 1673 0.96 (0.80-1.14) Song et al. 2007 763 852 1.12 (0.90-1.39) Jakubowska et al. 2009 318 290 0.93 (0.69-1.48) Colleran et al. 2009 960 958 0.93 (0.75-1.15) Joshi, 2011 (a) 167 222 0.69 (0.18-1.92) Joshi, 2011 (b) 42 169 1.85 (0.84-4.05) Meta-analyses Number of subjects Odds ratio Author (Year) Case Control CI=95% Kaklamani et al. 2006 1589 1.48 (1.11-1.96) Pasche et al. 1420 3451 1.38 (1.14-1.67) Cox et al. 3459 4557 1.10 (0.89-1.38) Colleran et al. 4669 5860 1.12 (0.97-1.31)† Liao et al. 5534 6605 1.16 (1.01-1.34)† Wang et al. 6421 7647 1.15 (1.01-1.31) 5799 5168 1.33 (1.02-1.73)† Ou et al. 6089 8103 1.12 (0.97-1.31)† Krishna et al. Fig. 2 Studies investigating TGFBR1*6A association with breast cancer risk. Forest plot showing the number of subjects and odds ratios of a Case-control studies, and b Meta-analyses associating TGFBR1*6A to breast cancer risk. Plot a includes only case/control studies that are in Hardy–Weinberg equilibrium. CI conﬁdence interval, ɪ dominant association (p ≤ 0.01), ‡ additive association (p ≤ 0.05), † allelic association (p ≤ 0.05). npj Breast Cancer (2022) 84 Published in partnership with the Breast Cancer Research Foundation K. Agyemang et al. heterogeneity among different case/controls as another contri- kinase domain. Analyses of the TGF-β/SMAD signal transduction buting factor . Overall, most meta-analyses published to date system using SBE4-lux and 3TP-lux transcription reporters, and show an association between TGFBR1*6A and breast cancer risk. phosphorylated-SMAD2 and SMAD3 levels showed comparable 25,28 Further studies are warranted to establish the risk association and TGF-β/SMAD signaling levels for TGFBR1*6A and TGFBR1 cells . its relatedness to different population sub-groups. The absence of TGFBR1*6A inﬂuence on TGF-β signaling is consistent with the observed TGF-β independent effect on migration and invasion in MCF-7 cells. Investigations into TGF-β/ TGFBR1*6A FUNCTIONAL EFFECTS non-SMAD pathways revealed that TGFBR1*6A increases ERK1/2 Promotion of cell proliferation, migration, and invasion phosphorylation but shows no signiﬁcant inﬂuence on p38 and JNK activation . The upregulation of ERK1/2 phosphorylation is Physicochemical studies using R1B/L17 and HEK 293 cells revealed suggestive of MAP-kinase-mediated migration and invasion that the mature TGFBR1*6A and the wild-type TGFBR1 receptors (Fig. 3b). A similar increase in MAP-kinase activation has also have similar half-life, receptor turnover, and binding afﬁnity to 23,25 been correlated with increased invasion of TGFBR1*6A-transfected TGF-β ligand . In MCF-7 breast cancer cells stably transfected colorectal cancer cells. Zhou et al. showed that TGFBR1*6A with either TGFBR1 or TGFBR1*6A, TGF-β/SMAD signaling was expression enhances proliferation and invasion in stably trans- comparable . However, the stably transfected TGFBR1*6A MCF-7 fected SW48 and DLD1 cells accompanied by increased ERK1/2 breast cancer cells exhibited enhanced cell growth, migration, and 25,28 phosphorylation . In contrast to breast cancer cells, the study invasion . Importantly, TGFBR1*6A switched TGF-β anti- noted an increase in p38 activation in the colorectal cancer cells. proliferative effects into growth stimulatory effects in the MCF-7 In all, the identiﬁed functional and mechanistic responses in breast cancer cells. The TGFBR1*6A-mediated switch to growth colorectal cancer cells appears to be TGF-β-dependent , indicat- stimulation was independent of TGFBR1*6A kinase domain, ing cancer-type differences in response mechanisms. indicating a mechanism likely due to TGFBR1*6A signal peptide . Further investigations into the signaling network of TGFBR1*6A- A similar conclusion was also reached following the investigation induced MCF-7 cell migration and invasion using Affymetrix of TGFBR1*6A induction of migration and invasion. There was a GeneChip Human Genome U133 Plus 2.0 Array identiﬁed 1.2-fold and 1.7-fold increase in migration and invasion, respec- ARHGAP5 and FN1 as the key differentially expressed genes. The tively, in TGFBR1*6A MCF-7 cells when compared to TGFBR1 levels of ARHGAP5 and FN1 were downregulated in TGFBR1*6A transfected cells . In the MCF-7 cells expressing low TGFBR1*6A MCF-7 cells when assessed with RT-qPCR and western blot levels, there were 1.3 and 1.9 times increase in migration and assays . ARHGAP5 encodes the Rho GTPase activating protein 5 invasion respectively, when compared to the vector controls. Cells that negatively regulates the Rho-family of small GTPases. The expressing higher and intermediate TGFBR1*6A levels showed 1.8 Rho-family of small GTPases are small guanosine triphosphatases and 2.2 times increase in migration and invasion, respectively. The (GTPases) of the rat sarcoma (Ras) superfamily that function as induction of migration and invasion observed in the TGFBR1*6A molecular switches for cytoskeletal remodeling during cell cells were not affected by TGF-β stimulation, suggesting an division, cell-cell adhesion, cell contractility, migration, and underlying migration mechanism that is TGF-β independent . invasion. ARHGAP5 (P190B) and ARHGAP35 (P190A) are the main Similar observations of increased growth and invasion were regulators of the Ras homolog (Rho) family of actin-based reported in colorectal cancer cells that endogenously harbor regulators and are implicated in cellular adhesion, migration, TGFBR1*6A. Using SW48 (TGFBR1/TGFBR1) and DLD1 (TGFBR1/ 51,52 and invasion . ARHGAP5 silencing inhibits migration and TGFBR1*6A) cells, TGF-β treatment resulted in growth inhibition in invasion of AGS and MGC-803 gastric adenocarcinoma cells .In the TGFBR1/TGFBR1 SW48 cells while it resulted in growth colorectal cancer, however, ARHGAP5 is markedly overexpressed stimulation in the TGFBR1/TGFBR1*6A DLD1 cells . Stable in the liver of metastatic tissues compared to matched primary transfection of the colorectal cancer cells with TGFBR1*6A also tumor tissues . ARHGAP5 suppression leads to decreased wound exhibited enhanced proliferation when compared with the vector- healing, migration, and invasion in DLD1 and SW480 colorectal transfected cells , indicating that TGFBR1*6A has similar func- cancer cells . Similarly, ampliﬁcation of ARHGAP5 on the 14q12 tional outcomes in breast and colorectal cancer. chromosomal locus promotes the spreading and migration of Huh-7 hepatocellular carcinoma cells . FN1 encodes ﬁbronectin, a MECHANISM OF ACTION soluble glycoprotein that binds to the cell surface and extra- cellular matrix. It is involved in cell-cell adhesion, cell motility, and The functional and mechanistic understanding of tumor suscept- maintenance of cell shape. Positive stromal ﬁbronectin expression ibility alleles are important in establishing their clinical signiﬁ- is signiﬁcantly associated with low metastatic spread among cance. TGFBR1*6A function in breast cancer cells is postulated to patients with invasive breast carcinoma . On the other hand, be mediated by its cleaved signal peptide, which is 3-alanine relatively low expression of stromal ﬁbronectin correlates with shorter than the wild-type TGFBR1 signal peptide (Figs. 1, 3a) . lymph node metastasis, TNM stage, recurrence, and mortality . Thus far, available evidence using SBE4-lux and 3TP-lux luciferase Studies have also shown that tamoxifen-induced TGF-β expres- reporters suggest a mechanism that is not mediated by the sion regulates ﬁbronectin levels in a feedback loop. TGF-β mature TGFBR1*6A receptor. The SBE4-lux luciferase reporter treatment reduced the levels of tamoxifen-induced FN1 in MCF- assesses the binding of activated SMAD2/3/4 with SMAD binding 7 cells . Overall, the unbiased gene expression analysis revealed elements in the nucleus as a measure of TGF-β/SMAD activa- 28,49 that TGFBR1*6A alters the expression of known pro- and anti- tion . Likewise, 3TP-lux transcription reporter measures the metastatic effectors, which may contribute to its effect on breast binding of activated SMAD2/3/4 to three consecutive 12-O- cancer progression. tetradeca-noylphorbol-13-acetate (TPA) response elements (TREs) and a portion of the plasminogen inhibitor-1 (PAI-1) promoter 28,50 region . In a study that assessed the proliferation of MCF-7 cells PERSPECTIVES AND FUTURE DIRECTIONS expressing intermediate and high levels of TGFBR1*6A with Genetic association with breast cancer risk and progression kinase-inactivated domains, TGFBR1*6A enhanced cell prolifera- tion in both clones similar to the kinase-activated TGFBR1*6A MCF- The case/control studies and meta-analyses assessing TGFBR1*6A 7 clones. Inactivation of the kinase domain did not inﬂuence association with breast cancer risk suggest an association between TGFBR1*6A growth stimulation, indicating a cell proliferation TGFBR1*6A and risk for breast cancer, albeit with demonstrable 26,27,32,41 enhancing mechanism that is not affected by the TGFBR1*6A heterogeneity in odds ratios . They also suggest that Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 84 K. Agyemang et al. Fig. 3 Schematic showing the role of TGFBR1*6A signal peptide. a TGFBR1*6A protein translation and processing, the TGFBR1*6A signal peptide is cleaved between Ala30 and Leu31, whereas the wild-type TGFBR1 is cleaved between Ala33 and Leu34. Both TGFBR1*6A and TGFBR1 wild-type exhibit similar binding afﬁnity to TGFB ligand and stability (half-life). The TGFBR1*6A signal peptide also demonstrates similar protein targeting and translocation functions as the wild-type. b TGFBR1*6A intracellular signaling, TGFBR1*6A maintains intact TGF-β signaling to induce growth and migration in breast cancer cells. It shows similar TGF-β signaling as wild-type TGFBR1 but enhances phosphorylation of ERK1/2 to induce its tumor-promoting effects. 66–69 TGFBR1*6A-associated breast cancer risk may depend on ancestry, same ancestral population . On the other hand, family-based geographical location, and other confounding factors. In genetic case/control studies can help control for confounding population 59,70 association studies, there are primarily four possible explanations stratiﬁcation because of shared family pedigrees; albeit these that account for a statistical association between a genetic designs can be more difﬁcult to recruit. Thus, in a bid to polymorphism (e.g., TGFBR1*6A) and risk for a disease (e.g., breast conclusively establish the risk association between TGFBR1*6A and cancer). They include (i) the allele directly affects the actual breast cancer, future case/control studies should consider multi- disease phenotype/expression, (ii) the allele is in linkage center family-based population studies. For multicenter population-based case/control studies that disequilibrium (correlated) with the true disease allelic locus, (iii) there is a spurious association due to population stratiﬁcation or draw subjects from different self-reported races/ethnicities, careful 59,60 consideration should be given to methods such as (i) structured other confounders , and (iv) type II error. To assert a direct allelic effect on disease phenotype/expression, the study design association tests, (ii) principal components analyses (PCA), and (iii) multidimensional scaling (MDS) from GWAS ancestry to control must control for confounding factors such as population 63,71–73 stratiﬁcation that could lead to false-positive/spurious associa- genetic admixture and confounding effects . Additionally, tions. In general, ancestral population substructure is the main the effect sizes reported for nearly all complex genetic traits confounding concern contributing to spurious genetic associa- require large sample sizes for robust power and precise 61,62 tions . Other sources include age, other mutations, and estimation. Thus, future studies should be designed with the 13,59,63–65 disease-speciﬁc contributory factors . By design, range of magnitude of the effect, and allele frequencies observed population-based case/control studies such as those used in most in the published studies, and recruit cases and controls accord- ingly to enable well-designed and well-powered subgroup of the TGFBR1*6A case/control studies are susceptible to spurious risk associations due to their random or convenient selection of analyses of the contribution of the homozygous and heterozygous cases and controls from participants that may not belong to the TGFBR1*6A genotypes. This will allow assessment of TGFBR1*6A npj Breast Cancer (2022) 84 Published in partnership with the Breast Cancer Research Foundation K. Agyemang et al. risk association by age, ethnicity, family history, tumor subtypes, pathways and mediators can be traced using recent investiga- tumor grade/histology, other mutation (BRCA1/2) status, response tional tools such as Whole Transcriptome RNA Sequencing (RNA- to cancer treatment, and survival/mortality outcomes. When seq) and proteomic proﬁling techniques coupled with computa- compared to high (BRCA1 and BRCA2: odds ratios ranging from tional network analyses such as comparative gene ontology (GO) 5.0 to 10.6) and moderate (CHEK2 and ATM: odds ratios ranging enrichment analyses, ingenuity pathway analyses (IPA), and 4,74,75 96–98 from 2.1 to 2.5) penetrance gene variants , TGFBR1*6A has protein–protein interaction (PPI) network analyses . In all, the been described as low penetrance (<odds ratios <2.0) tumor development of a unique TGF-β gene response signature (TBRS) 27,36 susceptibility allele . Although low penetrance gene variants for TGFBR1*6A as well as the elucidation of a druggable target and SNPs are common, their genetic risk to complex diseases such from the TGFBR1*6A transcriptomic and proteomic studies will be as cancer are comparatively small; carriers of low penetrance valuable for the prognosis of at-risk individuals and clinical breast cancer gene variants are estimated to have less than 20% evaluation for precision medicine. lifetime risk of developing breast cancers . As such, low penetrance gene variants are postulated to exert their oncogenic CONCLUDING REMARKS inﬂuence through additive or multiplicative covariance interac- tions with other residual gene variants, whose presence or Genetic association studies have identiﬁed TGFBR1*6A as a high 76,77 absence can determine disease trait . Thus, the coupling of frequency, low penetrance breast cancer susceptibility allele in the TGFBR1*6A family-based case/control studies with the breast cancer patients of varied ethnic backgrounds and participants’ GWAS data will allow an additional estimation of geographical locations. Its oncogenic inﬂuence is attributed to the genome-wide composite association of TGFBR1*6A with breast the promotion of cell proliferation, migration, and invasion. cancer risk, as it relates to other gene variants and gene loci that Further investigations into the risk associated with individual are in linkage disequilibrium, polygenic association or epistatic TGFBR1*6A genotypes as well as their correlation with breast interaction. Altogether, the estimation of the genetic risk and cancer subtypes, disease progression (tumor grade), metastasis, polygenic risk score of TGFBR1*6A from a robust family-based and survival are needed to clarify the population-speciﬁc study will inform clinical guidelines for early detection and susceptibilities of TGFBR1*6A carriers. adoption of preventive measures. DATA AVAILABILITY TGFBR1*6A MECHANISM OF ACTION AND INFLUENCE ON TGF- Any additional dataset other than cited published data were available upon request β BIOMARKER AND DRUG DEVELOPMENT to the corresponding author. Over the last decade, efforts aimed at developing anti-TGF-β drugs has for the most part led to inconclusive pre-clinical and clinical Received: 18 January 2022; Accepted: 13 June 2022; results and serious adverse events. This is at least partly due to imprecise biomarkers and drug targets identiﬁed so far. For example, in a recent phase I study of LY3022859 (an anti-TGFBR2 kinase inactivating monoclonal antibody) in patients with advanced solid tumors including breast carcinomas, there were REFERENCES associated infusion-related reactions such as cytokine release 1. Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2019. CA Cancer J. Clin. 69, syndrome. Also, the TGF-β small molecule inhibitor galunisertib 7–34 (2019). (LY2157299) and several others acting as neutralizing monoclonal 2. Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2020. CA Cancer J. Clin. 70, 79,80 81 antibodies for TGFB1 and TGFB2, and protein traps for 7–30 (2020). 3. DeSantis, C. E., Miller, K. D., Goding, S. A., Jemal, A. & Siegel, R. L. Cancer statistics TGFBR1 and TGFBR2 invariably yielded promising but minimal for African Americans, 2019. CA Cancer J. Clin. 69, 211–233 (2019). clinical beneﬁts. In all, a key weakness is a difﬁculty to identify 4. Hu, C. et al. A population-based study of genes previously implicated in breast precise biomarkers that would tailor TGF-β inhibitors to a speciﬁc cancer. N. Engl. J. Med. 384, 440–451 (2021). patient population. Recent studies have proposed and developed 5. Metcalfe, K. et al. International trends in the uptake of cancer risk reduction stra- a TGF-β gene response signature (TBRS) to assess TGF-β signaling tegies in women with a BRCA1 or BRCA2 mutation. Br. J. Cancer 121,15–21 (2019). response as a biomarker for cancer predisposition, clinical 6. Couch, F. J. et al. Associations between cancer predisposition testing panel genes outcome, and therapeutic response. It utilizes up to 153 genes and breast cancer. JAMA Oncol. 3, 1190–1196 (2017). as probes, which include mainly the TGF-β superfamily ligands 7. Zhang, H., Ahearn, T.U., Lecarpentier, J. & Barnes, D. Genome-wide association (TGFB1, BMP2), TGF-β receptors (TGFBR1 and TGFBR2), transcription study identiﬁes 32 novel breast cancer susceptibility loci from overall and subtype-speciﬁc analyses. Nat. Genet. 52, 572–581 (2020). factors (BACH1, TXNIP, and CREB1), and TGF-β responsive genes 83–85 83 8. Ferreira, M. A. et al. Genome-wide association and transcriptome studies identify (ID1, HMOX1, MMP2, and ZEB1) . Padua, 2008 and Wahdan- 85 target genes and risk loci for breast cancer. Nat. Commun. 10, 1741 (2019). Alaswad 2016 , used the TGF-β gene response signature (TBRS) 9. Barnes, D. R. et al. Polygenic risk scores and breast and epithelial ovarian cancer to classify breast tumors as TGF-β gene responsive (TBRS+) and risks for carriers of BRCA1 and BRCA2 pathogenic variants. Genet. Med. 22, TGF-β gene unresponsive (TBRS-) breast tumor subtypes and 1653–1666 (2020). 83 85 found a higher correlation between TBRS+, and ER- and TNBC 10. Ho, W. K. & Tan, M. M. European polygenic risk score for prediction of breast cancer breast tumor subtypes. Additionally, recent studies relate TGF-β shows similar performance in Asian women. Nat. Commun. 11, 3833 (2020). signaling with several cross-talk pathways including microRNA 11. Desmond, A. et al. Clinical actionability of multigene panel testing for hereditary synthesis, stromal ﬁbrosis, and endoplasmic reticulum (ER) stress breast and ovarian cancer risk assessment. JAMA Oncol. 1, 943–951 (2015). 12. Hauke, J. et al. Gene panel testing of 5589 BRCA1/2-negative index patients with that regulate immune checkpoint inhibitors, stem cell formation, 86–90 breast cancer in a routine diagnostic setting: results of the German Consortium and metastasis . for Hereditary Breast and Ovarian. Cancer 7, 1349–1358 (2018). The question of whether TGFBR1*6A functions singularly or in 13. Narod, S. A. Modiﬁers of risk of hereditary breast cancer. Oncogene 25, 5832–5836 combination with other pro-oncogenic pathways to signal (2006). modiﬁcations in oncogenic traits needs to be answered to provide 14. Buys, S. S. et al. A study of over 35,000 women with breast cancer tested with a clues for tailored drug and biomarker development for the 25-gene panel of hereditary cancer genes. Cancer 123, 1721–1730 (2017). 91–95 affected TGFBR1*6A individuals . First, it is imperative to ﬁrmly 15. Massague, J. TGFbeta in cancer. Cell 134, 215–230 (2008). establish whether TGFBR1*6A signaling is a signal peptide or 16. Shi, M. et al. Latent TGF-β structure and activation. Nature 474, 343–349 (2011). receptor-mediated in various breast cancer subtypes and cell lines. 17. Principe, D. R. et al. TGF-β: duality of function between tumor prevention and carcinogenesis. J. Natl Cancer Inst. 106, djt369 (2014). Subsequent interactions and networking with identiﬁed cross-talk Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 84 K. Agyemang et al. 18. Moore-Smith, L. & Pasche, B. TGFBR1 signaling and breast cancer. J. Mammary 49. Zhou, S., Zawel, L., Lengauer, C., Kinzler, K. W. & Vogelstein, B. Characterization of Gland Biol. Neoplasia 16,89–95 (2011). human FAST-1, a TGF beta and activin signal transducer. Mol. Cell 2,121–127 (1998). 19. Feng, X. H. & Derynck, R. Speciﬁcity and versatility in tgf-beta signaling through 50. Wrana, J. L. et al. TGF beta signals through a heteromeric protein kinase receptor Smads. Annu. Rev. Cell Dev. Biol. 21, 659–693 (2005). complex. Cell 71, 1003–1014 (1992). 20. Tzavlaki, K. & Moustakas, A. TGF-β Signaling. Biomolecules 10 (2020). 51. Ponik, S. M., Trier, S. M., Wozniak, M. A., Eliceiri, K. W. & Keely, P. J. RhoA is down- 21. Moustakas, A. & Heldin, C. H. Non-Smad TGF-beta signals. J. Cell Sci. 118, regulated at cell-cell contacts via p190RhoGAP-B in response to tensional 3573–3584 (2005). homeostasis. Mol. Biol. Cell 24, 1688–1699 (2013). s1-3. 22. Pasche, B. et al. Type I transforming growth factor beta receptor maps to 9q22 52. Stiegler, A. L. & Boggon, T. J. The N-terminal GTPase domain of p190RhoGAP and exhibits a polymorphism and a rare variant within a polyalanine tract. Cancer proteins is a pseudoGTPase. Structure 26, 1451–1461.e4 (2018). Res. 58, 2727–2732 (1998). 53. Dong, G. et al. SIRT1 suppresses the migration and invasion of gastric cancer by 23. Pasche, B. et al. TbetaR-I(6A) is a candidate tumor susceptibility allele. Cancer Res. regulating ARHGAP5 expression. Cell Death Dis. 9, 977 (2018). 59, 5678–5682 (1999). 54. Tian, T. et al. Investigation of the role and mechanism of ARHGAP5-mediated 24. Ruddon, R. W. Progress in molecular biology and translational science. Preface. colorectal cancer metastasis. Theranostics 10, 5998–6010 (2020). Prog. Mol. Biol. Transl. Sci. 95, xi (2010). 55. Gen, Y. et al. A novel ampliﬁcation target, ARHGAP5, promotes cell spreading and 25. Pasche, B. et al. Somatic acquisition and signaling of TGFBR1*6A in cancer. JAMA migration by negatively regulating RhoA in Huh-7 hepatocellular carcinoma cells. 294, 1634–1646 (2005). Cancer Lett. 275,27–34 (2009). 26. Kaklamani, V. G. et al. Combined genetic assessment of transforming growth 56. Christensen, L., Nielsen, M., Andersen, J. & Clemmensen, I. Stromal ﬁbronectin factor-beta signaling pathway variants may predict breast cancer risk. Cancer Res. staining pattern and metastasizing ability of human breast carcinoma. Cancer Res. 65, 3454–3461 (2005). 48, 6227–6233 (1988). 27. Wang, Y. Q., Qi, X. W., Wang, F., Jiang, J. & Guo, Q. N. Association between TGFBR1 57. Takei, H. et al. Angiogenesis and stromal ﬁbronectin expression in invasive breast polymorphisms and cancer risk: a meta-analysis of 35 case-control studies. PLoS carcinoma. Int. J. Oncol. 12, 517–523 (1998). ONE 7, e42899 (2012). 58. Horii, Y. et al. The regulatory effect of tamoxifen on ﬁbronectin expression in 28. Rosman, D. S., Phukan, S., Huang, C. C. & Pasche, B. TGFBR1*6A enhances the estrogen-dependent MCF-7 breast carcinoma cells. Oncol. Rep. 15, 1191–1195 (2006). migration and invasion of MCF-7 breast cancer cells through RhoA activation. 59. Bhandari, V. Genetic Inﬂuences in Lung Development and Injury. In The Newborn Cancer Res. 68, 1319–1328 (2008). Lung: Neonatology Questions and Controversies (ed. Bancalari, E.), Chapter 2, 29. Abecasis, G. R. et al. A map of human genome variation from population-scale 29–55 (Saunders, Philadelphia, PA, 2012). sequencing. Nature 467, 1061–1073 (2010). 60. Tasha E., F. & David A., S. Genetics of Lung Disease. In Murray and Nadel’s Text- 30. Abecasis, G. R. et al. An integrated map of genetic variation from 1,092 human book of Respiratory Medicine, Vol. 1, part 1, Section A (ed. Courtney, B. V.), Chap- genomes. Nature 491,56–65 (2012). ter 3, 35–37 (Elsevier, Philadelphia, PA, 2016). 31. Auton, A. et al. A global reference for human genetic variation. Nature 526,68–74 61. Sirugo, G., Williams, S. M. & Tishkoff, S. A. The missing diversity in human genetic (2015). studies. Cell 177, 1080 (2019). 32. Pasche, B. et al. TGFBR1*6A and cancer: a meta-analysis of 12 case-control stu- 62. Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for dies. J. Clin. Oncol. 22, 756–758 (2004). complex traits. Nature 570, 514–518 (2019). 33. Chen, T. et al. Int7G24A variant of transforming growth factor-beta receptor type I 63. Coignard, J., Lush, M., Beesley, J. & O’Mara, T.A. A case-only study to identify is associated with invasive breast cancer. Clin. Cancer Res. 12, 392–397 (2006). genetic modiﬁers of breast cancer risk for BRCA1/BRCA2 mutation carriers. Nat. 34. Feigelson, H. S. et al. Transforming growth factor beta receptor type I and trans- Commun. 12, 1078 (2021). forming growth factor beta1 polymorphisms are not associated with post- 64. Sekhar, D., Pooja, S., Kumar, S. & Rajender, S. RAD51 135G>C substitution menopausal breast cancer. Cancer Epidemiol. Biomark. Prev. 15,1236–1237 (2006). increases breast cancer risk in an ethnic-speciﬁc manner: a meta-analysis on 35. Cox, D. G., Penney, K., Guo, Q., Hankinson, S. E. & Hunter, D. J. TGFB1 and TGFBR1 21,236 cases and 19,407 controls. Sci. Rep. 5, 11588 (2015). polymorphisms and breast cancer risk in the Nurses’ Health Study. BMC Cancer 7, 65. Chen, X. et al. Associations between RAD51D germline mutations and breast 175 (2007). cancer risk and survival in BRCA1/2-negative breast cancers. Ann. Oncol. 29, 36. Baxter, S. W., Choong, D. Y., Eccles, D. M. & Campbell, I. G. Transforming growth 2046–2051 (2018). factor beta receptor 1 polyalanine polymorphism and exon 5 mutation analysis in 66. Kadouri, L. et al. A single-nucleotide polymorphism in the RAD51 gene modiﬁes breast and ovarian cancer. Cancer Epidemiol. Biomark. Prev. 11, 211–214 (2002). breast cancer risk in BRCA2 carriers, but not in BRCA1 carriers or noncarriers. Br. J. 37. Song, B. et al. TGFBR1(*)6A and Int7G24A variants of transforming growth factor- Cancer 90, 2002–2005 (2004). beta receptor 1 in Swedish familial and sporadic breast cancer. Br. J. Cancer 97, 67. Antoniou, A. C. et al. RAD51 135G–>C modiﬁes breast cancer risk among BRCA2 1175–1179 (2007). mutation carriers: results from a combined analysis of 19 studies. Am. J. Hum. 38. Jin, Q. et al. Polymorphisms and haplotype structures in genes for transforming Genet. 81, 1186–1200 (2007). growth factor beta1 and its receptors in familial and unselected breast cancers. 68. Wang, W. W. et al. A single nucleotide polymorphism in the 5’ untranslated Int. J. Cancer 112,94–99 (2004). region of RAD51 and risk of cancer among BRCA1/2 mutation carriers. Cancer 39. Jakubowska, A. et al. BRCA1-associated breast and ovarian cancer risks in Poland: Epidemiol. Biomark. Prev. 10, 955–960 (2001). no association with commonly studied polymorphisms. Breast Cancer Res. Treat. 69. Levy-Lahad, E. et al. A single nucleotide polymorphism in the RAD51 gene 119, 201–211 (2010). modiﬁes cancer risk in BRCA2 but not BRCA1 carriers. Proc. Natl Acad. Sci. USA 98, 40. Colleran, G. et al. The TGFBR1*6A/9A polymorphism is not associated with dif- 3232–3236 (2001). ferential risk of breast cancer. Breast Cancer Res. Treat. 119, 437–442 (2010). 70. Zondervan, K. T. & Cardon, L. R. Designing candidate gene and genome-wide 41. Joshi, N. N., Kale, M. D., Hake, S. S. & Kannan, S. Transforming growth factor β case-control association studies. Nat. Protoc. 2, 2492–2501 (2007). signaling pathway associated gene polymorphisms may explain lower breast 71. Tian, C., Gregersen, P. K. & Seldin, M. F. Accounting for ancestry: population sub- cancer risk in western Indian women. PLoS ONE 6, e21866 (2011). structure and genome-wide association studies. Hum. Mol. Genet. 17,R143–R150 42. Kamali, E., Hemmati, S., Safari, F. & Tavassoli, M. TGFBR1 polymorphism and risk of (2008). breast cancer in Iranian women. Int. J. Biol. Markers 30, e414–e417 (2015). 72. Price, A. L. et al. Principal components analysis corrects for stratiﬁcation in 43. Durbin, R. M. et al. A map of human genome variation from population-scale genome-wide association studies. Nat. Genet. 38, 904–909 (2006). sequencing. Nature 467, 1061–1073 (2010). 73. Helgason, A., Yngvadóttir, B., Hrafnkelsson, B., Gulcher, J. & Stefánsson, K. An 44. Kaklamani, V. G. et al. TGFBR1*6A and cancer risk: a meta-analysis of seven case- Icelandic example of the impact of population structure on association studies. control studies. J. Clin. Oncol. 21, 3236–3243 (2003). Nat. Genet. 37,90–95 (2005). 45. Liao, R. Y. et al. TGFBR1*6A/9A polymorphism and cancer risk: a meta-analysis of 74. Narod, S. A. Which genes for hereditary breast cancer? N. Engl. J. Med. 384, 13,662 cases and 14,147 controls. Mol. Biol. Rep. 37, 3227–3232 (2010). 471–473 (2021). 46. Ou, C. et al. Meta-analysis of transforming growth factor β receptor I 6A/9A gene 75. Dorling, L. et al. Breast cancer risk genes - association analysis in more than polymorphism and breast cancer risk: the picture remains murky. Biomarkers 20, 113,000 women. N. Engl. J. Med. 384, 428–439 (2021). 487–494 (2015). 76. Mavaddat, N. et al. Polygenic risk scores for prediction of breast cancer and 47. Krishna, B.M., Jana, S. & Panda, A.K. Association of TGF-β1 polymorphisms with breast cancer subtypes. Am. J. Hum. Genet. 104,21–34 (2019). breast cancer risk: a meta-analysis of case-control studies †. Cancers 12, 471 77. Phillips, P. C. Epistasis–the essential role of gene interactions in the structure and (2020). evolution of genetic systems. Nat. Rev. Genet. 9, 855–867 (2008). 48. Zhou, R., Huang, Y., Cheng, B., Wang, Y. & Xiong, B. TGFBR1*6A is a potential 78. Tolcher, A. W. et al. A phase 1 study of anti-TGFβ receptor type-II monoclonal modiﬁer of migration and invasion in colorectal cancer cells. Oncol. Lett. 15, antibody LY3022859 in patients with advanced solid tumors. Cancer Chemother. 3971–3976 (2018). Pharm. 79, 673–680 (2017). npj Breast Cancer (2022) 84 Published in partnership with the Breast Cancer Research Foundation K. Agyemang et al. 79. de Gramont, A., Faivre, S. & Raymond, E. Novel TGF-β inhibitors ready for prime 98. Li, Y. et al. RNA-Seq and network analysis revealed interacting pathways in TGF- time in onco-immunology. Oncoimmunology 6, e1257453 (2017). β-treated lung cancer cell lines. Cancer Inf. 13, 129–140 (2014). 80. Morris, J. C. et al. Phase I study of GC1008 (fresolimumab): a human anti- transforming growth factor-beta (TGFβ) monoclonal antibody in patients with advanced malignant melanoma or renal cell carcinoma. PLoS ONE 9, e90353 (2014). ACKNOWLEDGEMENTS 81. Zhu, H. et al. A novel TGFβ trap blocks chemotherapeutics-induced TGFβ1signaling The authors acknowledge Michael Olivier, Ph.D. (Center for Precision Medicine, and enhances their anticancer activity in gynecologic cancers. Clin. Cancer Res. 24, Department of Internal Medicine, Wake Forest Baptist Health, Winston-Salem, 2780–2793 (2018). USA) for his contribution to discussing parts of the manuscript. All images in 82. Muraoka, R. S. et al. Blockade of TGF-beta inhibits mammary tumor cell viability, Figs. 1 and 3 were created with Biorender (https://biorender.com/) online visual migration, and metastases. J. Clin. Invest. 109, 1551–1559 (2002). drawing tool. 83. Padua, D. et al. TGFbeta primes breast tumors for lung metastasis seeding through angiopoietin-like 4. Cell 133,66–77 (2008). 84. Lehmann, B. D. et al. Identiﬁcation of human triple-negative breast cancer sub- AUTHOR CONTRIBUTIONS types and preclinical models for selection of targeted therapies. J. Clin. Invest. All authors contributed in parts to the conception, writing, and revision of the 121, 2750–2767 (2011). manuscript. The ﬁrst author K.A and corresponding author B.P conceived and wrote 85. Wahdan-Alaswad, R. et al. Metformin attenuates transforming growth factor beta the manuscript. The co-authors A.M.J, G.W.B, M.J.P, K.S., H.J., and C.B., contributed to (TGF-β) mediated oncogenesis in mesenchymal stem-like/claudin-low triple discussing and revising the cancer genetics sections, S.S., P.A.F., R.S., K.L.C., H.-K.L., negative breast cancer. Cell Cycle 15, 1046–1059 (2016). W.Z., H.-W.L., K.W., and P.S. contributed to discussing and revising the molecular 86. Vanpouille-Box, C. et al. TGFβ is a master regulator of radiation therapy-induced signaling sections, and C.D.L. contributed to discussing and revising the cancer antitumor immunity. Cancer Res. 75, 2232–2242 (2015). genetics and genome-wide association (GWAS) implications of the article. 87. Zhang, D. et al. Proteomic characterization of differentially expressed proteins in breast cancer: expression of hnRNP H1, RKIP and GRP78 is strongly associated with HER-2/neu status. Proteom. Clin. Appl. 2,99–107 (2008). COMPETING INTERESTS 88. Scriven, P. et al. Activation and clinical signiﬁcance of the unfolded protein response in breast cancer. Br. J. Cancer 101, 1692–1698 (2009). The authors declare no competing interests. 89. Bai, W. D. et al. MiR-200c suppresses TGF-β signaling and counteracts trastuzu- mab resistance and metastasis by targeting ZNF217 and ZEB1 in breast cancer. Int J. Cancer 135, 1356–1368 (2014). ADDITIONAL INFORMATION 90. Wang, S. et al. MicroRNA‑133b targets TGFβ receptor I to inhibit TGF‑β‑induced Correspondence and requests for materials should be addressed to Boris Pasche. epithelial‑to‑mesenchymal transition and metastasis by suppressing the TGF‑β/ SMAD pathway in breast cancer. Int J. Oncol. 55, 1097–1109 (2019). Reprints and permission information is available at http://www.nature.com/ 91. Albain, K. S. et al. Prognostic and predictive value of the 21-gene recurrence score reprints assay in postmenopausal women with node-positive, oestrogen-receptor- positive breast cancer on chemotherapy: a retrospective analysis of a rando- Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims mised trial. Lancet Oncol. 11,55–65 (2010). in published maps and institutional afﬁliations. 92. Dowsett, M. et al. Prediction of risk of distant recurrence using the 21-gene recurrence score in node-negative and node-positive postmenopausal patients with breast cancer treated with anastrozole or tamoxifen: a TransATAC study. J. Clin. Oncol. 28, 1829–1834 (2010). Open Access This article is licensed under a Creative Commons 93. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, Attribution 4.0 International License, which permits use, sharing, 646–674 (2011). adaptation, distribution and reproduction in any medium or format, as long as you give 94. Milanese, J. S. et al. Germline variants associated with leukocyte genes predict appropriate credit to the original author(s) and the source, provide a link to the Creative tumor recurrence in breast cancer patients. NPJ Precis. Oncol. 3, 28 (2019). Commons license, and indicate if changes were made. The images or other third party 95. Xu, X. et al. Association of germline variants in natural killer cells with tumor material in this article are included in the article’s Creative Commons license, unless immune microenvironment subtypes, tumor-inﬁltrating lymphocytes, immu- indicated otherwise in a credit line to the material. If material is not included in the notherapy response, clinical outcomes, and cancer risk. JAMA Netw. Open 2, article’s Creative Commons license and your intended use is not permitted by statutory e199292 (2019). regulation or exceeds the permitted use, you will need to obtain permission directly 96. Foroutan, M., Cursons, J., Hediyeh-Zadeh, S., Thompson, E. W. & Davis, M. J. A from the copyright holder. To view a copy of this license, visit http:// transcriptional program for detecting TGFβ-induced EMT in cancer. Mol. Cancer creativecommons.org/licenses/by/4.0/. Res. 15, 619–631 (2017). 97. Wang, X., Liu, Q. & Zhang, B. Leveraging the complementary nature of RNA-Seq and shotgun proteomics data. Proteomics 14, 2676–2687 (2014). © The Author(s) 2022 Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 84
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