The prognostic value of gene alterations in colon cancer patients: An analysis of the cBioPortal database

doi:10.21203/rs.3.rs-2117393/v1

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The prognostic value of gene alterations in colon cancer patients: An analysis of the cBioPortal database

https://doi.org/10.21203/rs.3.rs-2117393/v1

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Background: Colorectal cancer is the third most common cancer worldwide, with colon cancer representing 66.7% of cases. Treatment consists of surgical resection and chemotherapy; however, this is less effective at the later stages of the disease. Gene targeted therapies represent a promising treatment option and, where available, show a significant improvement in overall patient survival. The present study investigated the genetic alteration occurring in colon cancer and used this to produce a predictive model for patient overall survival.

Methods: The data was obtained from the cBioPortal database (cbioportal.org) and was used to analyse genetic alteration in 1272 colon cancer patients. Kaplan-Meyer analysis was used to evaluate the effect of specific gene alteration and patient information on overall survival.

Results: Genetic alteration to either APC, PTPRT, SRC, and TP53 were associated with improved patient overall survival compared to wild-type. Conversely, alteration to BRAF, CSMD1, FBXW7, MACROD2, RBFOX1, SYNE1, TTN, and WWOX were associated with worse overall survival. Patient data indicated that BMI, sex, TMB, and stage at diagnosis had no significant effect in this population, whilst an age greater than 60 years and right sided cancer was associated with a worse overall survival irrespective of genetic alteration. These factors were used to produce a predictive model for colon cancer overall survival.

Conclusions: This study provides a partially predictive model for clinical research to estimate overall survival in colon cancer patients. Understanding the precise clinical significance of tumour genetics will result in a more tailored approach to the treatment of the disease.

Colon cancer

cBioPortal

Mutation

Overall survival

Personalised medicine

Epidemiology and Aetiology

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer death worldwide [1]. Of CRC patients, 66.7% have colon cancer (CCA), with the majority (70%) resulting from lifestyle and environmental factors as opposed to familial clustering (20%) or inherited (10%) cases [2-4,1]. Individuals participating in physical activity, fibre-rich diet, or taking certain supplements and drugs (such as vitamin D, statins, and aspirin) have a 0.5 – 0.8-fold reduction of developing CCA [5,6,4]. In contrast, the risk increases by 1.2 to 2-fold in people with diabetes, alcoholism, history of smoking, and obesity [7-10].

Pathophysiology and Diagnosis

CCA is caused by an accumulation of gene alterations in cells of the colonic's epithelium, transforming it into a precancerous lesion (90% adenomas) and later to invasive cancer [1]. The most frequent genetic alteration in CCAs are missense mutations, leading to constitutive activity or loss of function for the effected protein [11,12]. Around 80% of CRC patients are diagnosed by colonoscopy and tissue biopsy after complaining of haematochezia, abdominal pain, or unexplained anaemia [13,1]. The remaining patients are asymptomatic and discovered in routine CRC screening or accidentally during surgical procedures [1]. In the UK, the CRC routine screening starts at the age of 45, and includes a guaiac-based faecal occult blood, faecal immunochemical test (FIT), and FIT-DNA test [1,14]. The incidence of diagnosing CCA in patients younger than 50 years old has been rising with the use of these routine screening protocols [15,16].

Treatment, Management, and Prognoses

For localised non-metastatic CCA, surgical resection is the gold standard treatment at any age [1]. If lymph nodes are involved, adjuvant therapy must be used (e.g., oxaliplatin). In patients who cannot undergo surgery or patients with metastasis, systemic chemotherapy can improve life quality and prolong life expectancy [1,17]. In addition, targeted treatments, such as cetuximab, which targets epidermal growth factor receptor (EGFR), significantly improved the overall survival in CRC patients by ten months [18]. However, cetuximab is not effective in all patients.

According to the United States National Institutes of Health, CRC patients between 2011 and 2017 had, on average, a 5-year survival rate of 64.7% [19]. Factors like tumour location, metastasis, and the underlying mutated genes are the major determinants of overall survival for CCA patients. The most common sites for metastasis in CCA are the liver, lungs, bones, and brain [20]. As they are difficult to treat, metastasised CCAs (Stage IV) are associated with a 14% worse 5-years [13,21]. Regarding gene alterations, a CCA with adenomatous polyposis coli (APC) alteration will have around 17% improvement in 5-year overall survival [22]. Conversely, an F-Box and WD repeat domain containing 7 (FBXW7) alteration will worsen the 5-year overall survival in CCA patients by 10% [23].

This study aimed to analyse the cBioPortal database to identify additional genetic markers that could provide a model to predict overall survival in CCA. It is proposed that observing trends in large datasets will inform treatment decisions, resulting in personalised therapy following genetic profiling of a patient.

Patient Data

Patient data was obtained from cBioPortal (cbioportal.org), an open-access cancer genomic database containing over 200 cancer studies with samples sequenced at the time of diagnosis [24,25]. All patients with CRC data were downloaded, and due to the documented survival difference between CCA and rectal cancer, this study focussed on CCA [26]. Exclusion criteria were applied to specify CCA patients with known overall survival (Figure 1). These criteria resulted in 1272 CCA patients from three studies [27-29]. Genetic alteration was defined as amplifications, deletions, inframe, missense, splice, truncation, and mixed mutations. These alterations excluded copy number variations, fusions and large genome rearrangement.

Genes’ selection and processing

Using the included CCA patient data, a total of 72 genes with alterations present in more than 5% of patients were observed. Detailed data for these genes were downloaded and processed using the IBM SPSS Statistics package [30]. This was to determine the significant difference between wild-type and altered type for each gene.

Estimation of the tumour mutational burden

In the current study, tumour mutational burden (TMB) was defined as the number of altered genes in the 72 genes investigated. Burden was arbitrarily separated into low (≤5 alterations), intermediate (6 to 10 alterations), and high (>10 alterations).

Statistical Analysis

IBM SPSS Statistics package was used for all statistical analysis. Data parametric status was determined using the Shapiro‑Wilk normality test. The significance of the difference between the gene types (wild-type and altered) was determined using the Wilcoxon test. For the significant genes with a prevalence of greater than 10%, further analyses were performed to determine the effect of age, sex, body-mass-index (BMI), stage at diagnosis, TMB, and their significance across the sample. The significance of these characteristics were determined using crosstabulations and the chi-square test. A p-value ≤ 0.05 was considered statistically significant. In Minitab [31], survival plots were produced using Kaplan-Meyer analysis. The data from cBioPortal database does not require ethical approval due to the public nature of the data.

Patient characteristics

This study included a total of 1272 CCA patients. Of these patients, there was a mean age of 60.2 years, with a slight male predominance (52%). The average patient BMI is 28.3 Kg/m², while the average TMB was 5.3 mutations, and 57.9% were diagnosed at stage IV (Table 1). An age greater than 60 years was the only patient characteristic significantly affecting the overall survival. Patients younger than 60 years had an improved 5-year survival by 4.6% (15.8% vs. 11.2%, p=0.001, Figure 2). Although the sex and TMB data were available for all the patients in the sample, these characteristics did not significantly affect overall survival (p=0.573 and 0.967, respectively, Figures 3 and 4).

Selected Genes

Review of the cBioPortal database identified 12 genes with an alteration rate ≥ 5% and with a significant effect on overall survival (p≤0.05). These genes are either tumour suppressor genes or oncogenes, highlighting their importance in CCA development and progression. The most commonly altered gene in this cohort is APC, with an alteration rate of 56.9% (p<0.001), followed by tumour protein 53 (TP53), with an alteration rate of 50.4% (p<0.001). All of the 12 genes and their significance are shown in Table 2. This study focusses on the genetic alterations with a prevalence of >10%. Stage at diagnosis, TMB and age at presentation was associated with particular genotypes, however no effect was observed between genetic alteration and BMI or sex.

Alterations to APC, TP53, and PTPRT were associated with improved overall survival

Alterations within the genes APC, TP53, and protein tyrosine phosphatase receptor type T (PTPRT) were associated with a higher overall survival at both 1-year, and 5-year when compared to wild-type. Of the APC alterations, 93.2% were truncation mutations likely resulting in activation of the Wnt-pathway [32]. The alteration in APC improved the 5-year survival by 3.3% compared with the wild-type (14.8% vs. 11.5%, p<0.001, Figure 5). Furthermore, APC alterations were associated with younger patients (mean age of 58.4 years vs. 62.6 years, p<0.001), and higher TMB than wild-type tumours (6.4 mutations vs. 3.8 mutations, Table 3).

TP53 alterations were predominantly missense (63.0%) and truncation (26.7%) mutations, resulting in gene inactivation [33]. An improvement in the 5-year survival by 2.3% in the altered TP53 compared with the wild-type was observed (14.5% vs. 12.2%, p<0.001, Figure 6). TP53 alteration is significantly associated with younger patients (mean age of 57.3 years vs. 63.2 years, p<0.001) and a later stage at presentation than wild-type (stage IV: 62.3% vs. 48.6%, p=0.002). TMB was higher in the altered TP53 than the wild-type (5.7 mutations vs. 4.8 mutations, p<0.001, Table 4).

Alterations in PTPRT consisted predominantly of amplifications (41.5%), leading to potentiating the gene function, and missense mutations (40.0%), resulting in loss of protein function [34]. PTPRT alterations improved the 5-year survival by 5.7% compared with the wild-type (18.5% vs. 12.8%, p=0.045, Figure 7). This improved overall survival was the result of amplification of PTPRT, rather than missense mutation (25.9% vs. 11.5%). PTPRT alterations are significantly associated with higher TMB than the wild-type (11.7 mutations vs. 4.5 mutations, p<0.001). Additionally, most altered PTPRT patients were diagnosed in stage III (40.7%) whereas the majority of the wild-type patients were diagnosed in stage IV (60.5%, p<0.001, Table 5).

Alterations to BRAF, RBFOX1, and FBXW7 were associated with worse overall survival

Alterations within the genes BRAF, RBFOX1, and FBXW7 were associated with a lower overall survival at both 1-year, and 5-year than the wild-type. Missense mutations were 93.7% of BRAF alterations, of which 68.4% were the V600E, leading to a constitutively active BRAF protein [35-37]. Furthermore, 68.6% of the mutated cases had right-sided CCA, commonly associated with worse outcome [38]. The 5-year survival was 9.5% worse in the altered type than the wild-type (4.9% vs. 14.4%, p<0.001, Figure 8). Additionally, altered BRAF was significantly associated with higher TMB than wild-type (10.3 mutations vs. 4.6 mutations, p<0.001, Table 6).

The most observed alterations in RBFOX1 were deletions (90.9%), associated with a loss of protein function [39,40]. A decrease by 9.5% in the 5-year survival was observed with the altered RBFOX1 compared with the wild-type (4.9% vs. 14.4%, p=0.030, Figure 9). RBFOX1 alterations are significantly associated with older patients (68.5 years vs. 59.2 years, p<0.001), and a higher TMB than wild-type (6.8 mutations vs. 5.1 mutations, p=0.001). Additionally, more than half of altered RBFOX1 patients were diagnosed at stage II, while wild-type patients were most commonly diagnosed at stage IV (p<0.001, Table 7).

Of the FBXW7 alterations, 58% were missense mutations and 34.4% truncation mutations. Of the missense mutations 25.9% were R465X, a mutation that removes FBXW7 protein function [41-43]. The alteration in FBXW7 decreased the 5-year survival by 9.8% compared with the wild-type (4.6% vs. 14.4%, p=0.015, Figure 10). FBXW7 alterations were associated with older patients (63.8 years vs. 59.8 years, p=0.002), an earlier stage at diagnosis (stage IV: 43.4% vs. 59.9%, p=0.004) and a higher TMB than wild-type (11.9 mutations vs. 4.5 mutations, p<0.001, Table 8).

This study explored the genes with an alteration between 5% and 10% and a statistically significant differences in mean overall survival between wild-type and altered type was observed for some genes. Of those genes, SRC alterations increased the overall survival compared to the wild-type. Conversely, alteration in TTN, CSMD1, WWOX, MACROD2, and SYNE1 resulted in worse overall survival (Table 2).

Predictive model for CCA based on genetic alterations and patient factors

When genetic alteration and patient factors are considered together, a predictive model for assessing overall survival was made. Each factor in a patient was given a value of 1 (if it was associated with decreased overall survival) or –1 (if it was associated with increased overall survival), and these were totalled. Patients were classified into three groups based on the accumulation of risk factors: high (>1), intermediate (-1 to 1), or low (<-1). This correctly stratified the overall survival of patients as a cohort at every time point of CCA (Figure 11). Low risk patients had a mean survival of 43.9 months, showing a clear advantage to the intermediate (32.6 months) and high risk (27.7 months) cohorts. This correlation with overall survival was greater than that observed for the TNM system in this cohort, which did not reach significance (p=0.319, Figure 12). Currently the TNM (Tumour, Nodes, Metastasis) staging system is used in CCA to predict patient outcome. The discrepancy of the results presented here are likely due to insufficient patient numbers in the lower stages of CCA (I, II, and III).

Together, these results demonstrate that gene signatures and patient factors can be used to predict overall survival in CCA patients. These genes play a fundamental role in CCA pathophysiology and outcome.

The high mortality associated with CCA is the result of the late presentation common in the disease, and resistance to current treatment strategies [1,44]. With the development of sequencing, data regarding tumour development is accumulating and demonstrates the clear heterogeneity of CCA. How to use this information to benefit patients is of highest practical significance. In this study, the most frequently altered genes in CCA were chosen to produce a predictive model for overall survival. Results demonstrate that genetic alteration of APC, PTPRT, SRC, TP53, BRAF, CSMD1, FBXW7, MACROD2, RBFOX1, SYNE1, TTN, and WWOX are associated with altered patient overall survival in CCA. These results are clinically meaningful as they can be used to predict patient outcome and inform treatment strategies.

Patient characteristics and outcome

As cBioPortal data is a collective of multiple studies, several clinical details such as stage at presentation or treatment regimens were not available for assessment for all patients. The stage at diagnosis was available for 818 patients, while the BMI was available for 229 patients. Whilst the data is clearly not complete, sufficient data was available for assessment of these characteristics. Large impacts on overall survival would have been observed based on the data presented here. Non-significance observed is the result of genuine features of CCA or potentially the result of characteristics unique to the database itself.

The most significant patient characteristics at presentation observed in this cohort was age and the location of the cancer. Patients over 60 years of age were associated with a worse overall survival, and this is in line with the current understanding in CCA [45,46]. For location, right sided CCA was associated with a worse outcome as has been described elsewhere [38].

Whilst TMB showed no observed effect in this study, this may be the result of how this was calculated here. The appropriate definition of TMB is the total number of mutations per coding area of a tumour genome [47]. It is widely hypothesised that higher levels of mutations in tumour cells produce more neoantigens, which will be recognised by T-cells and destroyed [48]. This has been demonstrated to lead to a higher sensitivity to treatment with immune checkpoint inhibitors [49,50]. It is interesting to note that alterations in TTN were associated with worse overall survival in this study (Table 2) and has been closely associated with TMB in other studies [51]. Su et al. (2021) demonstrated that a TTN mutation was associated with a greater response to immune checkpoint inhibitors, supporting previous comments [52]. Furthermore, in CRC patients with low TMB, therapy based on irinotecan was associated with improved progression free survival compared to oxaliplatin-based chemotherapy (11.9 vs. 6.5 months) [53].

Tumour suppressor genes and patient outcome

Alterations in tumour suppressor genes, such as APC, TP53, and PTPRT, resulted in an improved 5-year survival. APC encodes for a large multidomain protein that plays a role in intercellular adhesion and microtubule function [54,55]. The primary function of APC is to negatively regulate the wingless (Wnt)-pathway by forming a complex with β-catenin [55,56,32]. This complex will phosphorylate β-catenin for proteasomal degradation, reducing transcription activation and controlling cell growth (Figure 13) [32,54].

This study observed APC alteration in 57% of CCA patients, in line with the literature (45 to 80%), APC is the most common mutated gene in CCA [56,55,32,57]. This study and the wider literature observed truncation mutations as the dominant alteration [32,54]. Truncation leads to activation of the Wnt-pathway and pooling of β-catenin, leading to activation of T-cell factor and lymphoid enhancer factor families, resulting in activation of cellular MYC Proto-Oncogene [55,32,58]. The improved survival associated with APC alteration is supported by the literature, with wild type APC more commonly associated with right sided CCA and a poorer outcome [59,22,60].

TP53 encodes for P53 protein, which halts cell division in the G1 phase. This results from activating p21, which inhibits cyclin-dependent kinases [33,61,62]. TP53 suppresses tumour formation by inducing cell apoptosis by activating Bax protein and death receptors, such as death receptor 5 (Figure 14) [62]. Missense mutations in the central DNA binding domain are the most common alterations in TP53 [62,33]. Losing TP53 function leads to the transition of benign adenoma to invasive carcinoma [63]. The expression of ATP-binding cassette subfamily B member 1 increased by altered TP53, leads to enhanced drug efflux and chemoresistance [64]. In this study, TP53 alteration significantly improved overall survival which is in line with previous studies [65,66].

PTPRT inhibits signal transducer and activator of transcription 3 (STAT3) and paxillin function (Figure 15) [67-69]. STAT3 is an oncogene that promotes cell proliferation and metastasis [67,68]. STAT3 function by promoting Bcl-XL and Cyclin D1 [67,68]. Paxillin is a signal transduction adaptor protein that promotes tumour growth, cell migration, and metastasis [67,69]. 27% of CRC have PTPRT alterations, with missense mutations as the most common alteration, leading to loss of its function and decrease the overall survival by 45% [67,70,68,34,69]. In contrast, PTPRT amplification will potentiate its tumour suppressive role and inhibit tumour growth [34]. Overall, this study observed a survival advantage when PRPRT alterations were investigated as a collective group, however it is important to note the specific difference that is attributed to types of mutation.

In contrast to the previous genes, alteration in tumour suppressor genes, such as FBXW7 resulted in a worse overall survival. FBXW7 encode for the recognition components in a complex that ubiquitinates defective proteins, especially proto-oncogenes, leading to its degradation (Figure 16) [23,71]. In the current study, 10.3% of FBXW7 were altered, aligning with the reported range of 6-10% in CRC cases [41,72,73]. Missense mutations accounted for 70% of FBXW7 alterations, with R465 being the most common site [74,23,75]. Loss of FBXW7 function due to missense mutations will accumulate its targeted proto-oncogenes, resulting in cancer development [41-43].

Oncogenes and patient outcome

The major oncogene investigated in this study was BRAF, encoding a serine/threonine-protein kinase [35,76]. It is part of the mitogen-activated protein kinase (MAPK) pathway, promoting cellular proliferation and survival and preventing apoptosis (Figure 17) [36]. BRAF is activated by the kinase activity of KRAS Proto-Oncogene in response to extracellular stimuli such as EGFR [35]. Activated BRAF will phosphorylate and activate MAPK 1 and 2, activating extracellular signal-regulated kinases (ERK). ERK will regulate several cellular activities and activate multiple transcription factors [37]. Aligning with the current study, BRAF is altered in around 10% of CRCs, with missense mutations as the most common alteration (~90%). The substitution of valine to glutamic acid at codon 600 (V600E) is the most predominant substitution [35,76,36]. Missense mutations will lead to constitutively active BRAF, activating downstream pathways, promoting cellular proliferation, survival, and resistance to chemotherapy [35-37].

Other gene alterations and patient outcome

RBFOX1, an RNA splicing promoter, encodes the splicing factor ataxin-2 binding protein 1 (A2BP1) [77-79]. A2BP1 binds to ataxin 2, which regulates microRNA processing by increasing mRNA stability and promoting translation [79,80]. This study observed deletions in 90.9% of the RBFOX1 alterations, which align with previous studies [77,39]. Deletions in RBFOX1 will lead to loss of gene function and abnormal post-transcriptional processing of many tumour suppressors and oncoproteins [39,40,78,81].

From these results it is important to not only consider genetic alteration, but also the specific alteration present. For example, PTPRT amplification was associated with improved overall survival and not missense mutation. Other studies have observed this for many of the genes described here, such as TP53 in non-small cell lung cancer [82].

Predictive model of overall survival and targeted therapies

Biological processes are deregulated in cancer due to accumulated genetic alterations. The mechanisms that cause cancer are numerous, but as shown here often converge on tumour suppressor gene inactivation or oncogenic activation. Genetic alterations can co-occur, working synergistically to drive tumour development and this work demonstrates that this can be used to predict overall survival [25]. The predictive model for CCA described here combines the effect of 12 genetic alterations, patient age at presentation, and location of tumour. Individually these factors have a small impact on overall survival, however in combination a more complete prediction of survival appears. Many cancers are associated with the use of predictive models as a clinical tool. For example, Non-Hodgkin's Lymphoma use the International Prognostic Index in order to stratify patients in clinical trials [83,84]. This work supports the use of a similar stratification occurring in trials surrounding CCA in order to more completely assess treatment success.

Chemotherapy represents the predominant treatment option for colorectal cancer (CRC), but only half of the patients benefit from these regimens [44]. With the development of targeted therapies, the treatment of cancer has entered the era of precision medicine. For instance, APC alterations can be targeted through ICG-001, a β-catenin inhibitor, halting the Wnt pathway activated in these cancers [85]. Similarly, TP53 alterations can be targeted by a bispecific antibody, which targets the mutant p53 peptide–HLA complex [86,87]. It is hoped that a more complete understanding of the relationship between genetic alteration and survival will lead to the identification of novel therapies that specifically target these changes in CCA. However, a potential limitation is that these identified targets may not translate to successful therapies. This work provides strong preclinical evidence for targeting these pathways in CCA. Therapies that account for cancer genetics are already commonly in use, and this is likely to be a substantial area of development over the coming years. The key evidence for genetic targeted therapies is summarised in Table 9 and 10 and represent a practical application of this work.

This study is a pure database analysis of clinical information from cBioPortal. To our knowledge, this is the first paper to demonstrate the predictive capacity of genetic alterations on overall survival in CCA using the cBioPortal database. Genetic characteristics should be included in future studies, particularly clinical trials that often do not assess genetic alterations when reviewing treatment success. This knowledge can help in developing more personalised treatment regimens for CCA patients.

Conflicts of interest: The authors declare that they have no conflict of interest.

Data Availability section: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Acknowledgements: The authors would like to thank Adam Thomas, David Qualtrough, and Hazem Atta for reviewing the manuscript prior to submission. Some figures were created with BioRender.

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Table 1 Patients’ characteristics
Characteristic	Total (n=1272)
Age	(n=1270)
Mean (St. Err)	60.2 (0.4)
Median (range) – yrs.	60 (13-93)
Distribution - no. (%)
<40	93 (7.3)
40-59	510 (40.2)
60-79	551 (43.4)
≥80	116 (9.1)
Sex - no. (%)	(n=1272)
Male	662 (52)
Female	610 (48)
BMI - Kg/m²	(n=229)
Mean (St. Err)	28.3 (0.4)
Median (range)	26.9 (14.7-52.1)
Distribution - no. (%)
<30	159 (69.4)
≥30	70 (30.6)
Stage at the diagnosis - no. (%)	(n=818)
Stage I	28 (3.4)
Stage II	127 (15.5)
Stage III	189 (23.1)
Stage IV	474 (57.9)
Tumour mutation burden	(n=1272)
Mean (St. Err)	5.3 (0.2)
Median (range)	4 (0-51)
Distribution - no. (%)
≤ 5	887 (69.7)
6 – 10	215 (16.9)
> 10	170 (13.4)
Abbreviations: St. Err – standard error of the mean; yrs. – years; BMI – body mass index.

Table 2 The significant selected genes and their significant difference
Gene	Gene Full Name	Type	No.	Percentages	Mean Overall Survival	P-value
*APC*	Adenomatous Polyposis Coli	WT	548	43.1%	30.3	< 0.001
*APC*	Adenomatous Polyposis Coli	AT	724	56.9%	37	< 0.001
*TP53*	Tumour Protein 53	WT	631	49.6%	31.2	< 0.001
*TP53*	Tumour Protein 53	AT	641	50.4%	37	< 0.001
*BRAF*	B-Raf Proto-Oncogene, Serine/Threonine Kinase	WT	1129	96.3%	35.2	< 0.001
*BRAF*	B-Raf Proto-Oncogene, Serine/Threonine Kinase	AT	143	12.2%	25.4	< 0.001
*RBFOX1*	RNA binding protein, fox-1 homolog	WT	1129	88.8%	35.1	0.030
*RBFOX1*	RNA binding protein, fox-1 homolog	AT	143	11.2%	26.6	0.030
*FBXW7*	F-Box and WD Repeat Domain Containing 7	WT	1141	89.7%	35	0.015
*FBXW7*	F-Box and WD Repeat Domain Containing 7	AT	131	10.3%	26.9	0.015
*PTPRT*	Protein tyrosine phosphatase receptor type T	WT	1142	89.8%	33.5	0.045
*PTPRT*	Protein tyrosine phosphatase receptor type T	AT	130	10.2%	39.7	0.045
*TTN*	Titin	WT	1153	90.6%	35.2	0.036
*TTN*	Titin	AT	119	9.4%	23.9	0.036
*CSMD1*	CUB And Sushi Multiple Domains 1	WT	1191	93.6%	34.8	0.010
*CSMD1*	CUB And Sushi Multiple Domains 1	AT	81	6.4%	23.9	0.010
*WWOX*	WW Domain Containing Oxidoreductase	WT	1195	93.9%	34.6	0.016
*WWOX*	WW Domain Containing Oxidoreductase	AT	77	6.1%	26.6	0.016
*MACROD2*	Mono-ADP Ribosylhydrolase 2	WT	1197	94.1%	34.7	0.014
*MACROD2*	Mono-ADP Ribosylhydrolase 2	AT	75	5.9%	24.5	0.014
*SRC*	SRC Proto-Oncogene, Non-Receptor Tyrosine Kinase	WT	1203	94.6%	33.2	0.008
*SRC*	SRC Proto-Oncogene, Non-Receptor Tyrosine Kinase	AT	69	5.4%	49.7	0.008
*SYNE1*	Spectrin Repeat Containing Nuclear Envelope Protein 1	WT	1204	94.7%	34.7	0.032
*SYNE1*	Spectrin Repeat Containing Nuclear Envelope Protein 1	AT	68	5.3%	23.4	0.032
Wilcoxon test was used to measure the p-value of the difference in overall survival. P-value ≤ 0.05 is statistically significant. Abbreviations: OS – Overall survival; WT – Wild-type; AT – Altered type. Genes with non-significant differences can be found in appendix table 1.

Table 3 Characteristics of patients with APC alterations and wild-type
Characteristic	Altered (n=724)	Wild-type (n=548)	Total (n=1272)
Age
Mean (St. Err)	58.4 (0.5)	62.6 (0.6)	60.2 (0.4)
Median (range) – yrs.	59 (20-93)	64 (13-90)	60 (13-93)
Distribution - no. (%)
<40	60 (8.3)	33 (6)	93 (7.3)
40-59	319 (44.1)	191 (34.9)	510 (40.2)
60-79	299 (41.4)	252 (46.1)	551 (43.4)
≥80	45 (6.2)	71 (13)	116 (9.1)
P-value	<0.001
Sex - no. (%)
Male	383 (52.9)	279 (50.9)	662 (52)
Female	341 (47.1)	269 (49.1)	610 (48)
P-value	0.482
BMI - Kg/m²
Mean (St. Err)	30.2 (2.7)	28.2 (0.4)	28.3 (0.4)
Median (range)	27.2 (20.6-48.3)	26.9 (14.7-52.1)	26.9 (14.7-52.1)
Distribution - no. (%)
<30	8 (72.7)	151 (69.3)	159 (69.4)
≥30	3 (27.3)	67 (30.7)	70 (30.6)
P-value	0.808
Stage at the diagnosis - no. (%)
Stage I	23 (3.8)	5 (2.4)	28 (3.4)
Stage II	95 (15.5)	32 (15.5)	127 (15.5)
Stage III	137 (22.4)	52 (25.1)	189 (23.1)
Stage IV	356 (58.3)	118 (57)	474 (57.9)
P-value	0.717
Tumour mutation burden
Mean (St. Err)	6.4 (0.2)	3.8 (0.2)	5.3 (0.2)
Median (range)	4 (0-51)	2 (0-33)	4 (0-51)
Distribution - no. (%)
≤ 5	460 (63.5)	427 (77.9)	887 (69.7)
6 - 10	158 (21.8)	57 (10.4)	215 (16.9)
> 10	106 (14.6)	64 (11.7)	170 (13.4)
P-value	<0.001
Statistically significant P-value (<0.05). Chi-square test was used to measure the p-value. Abbreviations: St. Err – standard error of the mean; yrs. – years; BMI – body mass index.

Table 4 Characteristics of patients with TP53 alterations and wild-type
Characteristic	Altered (n=641)	Wild-type (n=631)	Total (n=1272)
Age
Mean (St. Err)	57.3 (0.5)	63.2 (0.6)	60.2 (0.4)
Median (range) – yrs.	57 (20-93)	64 (13-93)	60 (13-93)
Distribution – no. (%)
<40	63 (9.8)	30 (4.8)	93 (7.3)
40-59	301 (47)	209 (33.2)	510 (40.2)
60-79	243 (38)	308 (48.9)	551 (43.4)
≥80	33 (5.2)	83 (13.2)	116 (9.1)
P-value	<0.001
Sex – no. (%)
Male	327 (51)	335 (53.1)	662 (52)
Female	314 (49)	296 (46.9)	610 (48)
P-value	0.459
BMI – Kg/m²
Mean (St. Err)	30.7 (2.5)	28.2 (0.4)	28.3 (0.4)
Median (range)	30.7 (19.1-41.2)	26.9 (14.7-52.1)	26.9 (14.7-52.1)
Distribution – no. (%)
<30	5 (50)	154 (70.3)	159 (69.4)
≥30	5 (50)	65 (29.7)	70 (30.6)
P-value	0.173
Stage at the diagnosis – no. (%)
Stage I	20 (3.6)	8 (3.1)	28 (3.4)
Stage II	75 (13.4)	52 (20.1)	127 (15.5)
Stage III	116 (20.8)	73 (28.2)	189 (23.1)
Stage IV	348 (62.3)	126 (48.6)	474 (57.9)
P-value	0.002
Tumour mutation burden
Mean (St. Err)	5.7 (0.2)	4.8 (0.3)	5.3 (0.2)
Median (range)	4 (0-49)	3 (0-51)	4 (0-51)
Distribution – no. (%)
≤ 5	432 (67.4)	455 (72.1)	887 (69.7)
6 – 10	136 (21.2)	79 (12.5)	215 (16.9)
> 10	73 (11.4)	97 (15.4)	170 (13.4)
P-value	<0.001
Statistically significant P-value (<0.05). Chi-square test was used to measure the p-value. Abbreviations: St. Err – standard error of the mean; yrs. – years; BMI – body mass index.

Table 5 Characteristics of patients with PTPRT alterations and wild-type
Characteristic	Altered (n=130)	Wild-type (n=1142)	Total (n=1272)
Age
Mean (St. Err)	61.2 (1.1)	60.1 (0.4)	60.2 (0.4)
Median (range) – yrs.	60 (35-88)	60 (13-93)	60 (13-93)
Distribution – no. (%)
<40	3 (2.3)	90 (7.9)	93 (7.3)
40-59	57 (44.2)	453 (39.7)	510 (40.2)
60-79	61 (47.3)	490 (42.9)	551 (43.4)
≥80	8 (6.2)	108 (9.5)	116 (9.1)
P-value	0.062
Sex – no. (%)
Male	69 (53.1)	593 (51.9)	662 (52)
Female	61 (46.9)	549 (48.1)	610 (48)
P-value	0.804
BMI – Kg/m²
Mean (St. Err)	30.3 (2.4)	28.2 (0.4)	28.3 (0.4)
Median (range)	27.9 (19.7-52.1)	26.8 (14.7-48.7)	26.9 (14.7-52.1)
Distribution – no. (%)
<30	9 (75)	150 (69.1)	159 (69.4)
≥30	3 (25)	67 (30.9)	70 (30.6)
P-value	0.667
Stage at the diagnosis – no. (%)
Stage I	5 (5.8)	23 (3.1)	28 (3.4)
Stage II	15 (17.4)	112 (15.3)	127 (15.5)
Stage III	35 (40.7)	154 (21)	189 (23.1)
Stage IV	31 (36)	443 (60.5)	474 (57.9)
P-value	<0.001
Tumour mutation burden
Mean (St. Err)	11.7 (0.8)	4.5 (0.2)	5.3 (0.2)
Median (range)	10 (1-51)	3 (0-41)	4 (0-51)
Distribution – no. (%)
≤ 5	35 (26.9)	852 (74.6)	887 (69.7)
6 – 10	34 (26.2)	181 (15.8)	215 (16.9)
> 10	61 (46.9)	109 (9.5)	170 (13.4)
P-value	<0.001
Statistically significant P-value (<0.05). Chi-square test was used to measure the p-value. Abbreviations: St. Err – standard error of the mean; yrs. – years; BMI – body mass index.

Table 6 Characteristics of patients with BRAF alterations and wild-type
Characteristic	Altered (n=143)	Wild-type (n=1129)	Total (n=1272)
Age
Mean (St. Err)	61.7 (1.3)	60.1 (0.4)	60.2 (0.4)
Median (range) – yrs.	62 (26-90)	60 (13-93)	60 (13-93)
Distribution – no. (%)
<40	15 (10.5)	78 (6.9)	93 (7.3)
40-59	50 (35)	460 (40.8)	510 (40.2)
60-79	60 (42)	491 (43.6)	551 (43.4)
≥80	18 (12.6)	98 (8.7)	116 (9.1)
P-value	0.141
Sex – no. (%)
Male	71 (49.7)	591 (52.3)	662 (52)
Female	72 (50.3)	538 (47.7)	610 (48)
P-value	0.543
BMI – Kg/m²
Mean (St. Err)	20 (0.9)	28.3 (0.4)	28.3 (0.4)
Median (range)	20 (19.1-20.9)	27.1 (14.7-52.1)	26.9 (14.7-52.1)
Distribution – no. (%)
<30	2 (100)	157 (69.2)	159 (69.4)
≥30	0 (0)	70 (30.8)	70 (30.6)
P-value	0.346
Stage at the diagnosis – no. (%)
Stage I	5 (4.1)	23 (3.3)	28 (3.4)
Stage II	20 (16.5)	107 (15.4)	127 (15.5)
Stage III	36 (29.8)	153 (22)	189 (23.1)
Stage IV	60 (49.6)	414 (59.4)	474 (57.9)
P-value	0.193
Tumour mutation burden
Mean (St. Err)	10.3 (0.7)	4.6 (0.2)	5.3 (0.2)
Median (range)	7 (1-51)	3 (0-49)	4 (0-51)
Distribution – no. (%)
≤ 5	61 (42.7)	826 (73.2)	887 (69.7)
6 – 10	26 (18.2)	189 (16.7)	215 (16.9)
> 10	56 (39.2)	114 (10.1)	170 (13.4)
P-value	<0.001
Statistically significant P-value (<0.05). Chi-square test was used to measure the p-value. Abbreviations: St. Err – standard error of the mean; yrs. – years; BMI – body mass index.

Table 7 Characteristics of patients with RBFOX1 alterations and wild-type
Characteristic	Altered (n=143)	Wild-type (n=548)	Total (n=1272)
Age
Mean (St. Err)	68.5 (1)	59.2 (0.4)	60.2 (0.4)
Median (range) – yrs.	70 (35-90)	59 (13-93)	60 (13-93)
Distribution – no. (%)
<40	3 (2.1)	90 (8)	93 (7.3)
40-59	29 (20.3)	481 (42.7)	510 (40.2)
60-79	80 (55.9)	471 (41.8)	551 (43.4)
≥80	31 (21.7)	85 (7.5)	116 (9.1)
P-value	<0.001
Sex – no. (%)
Male	70 (49)	592 (52.4)	662 (52)
Female	73 (51)	537 (47.6)	610 (48)
P-value	0.432
BMI – Kg/m²
Mean (St. Err)	29.1 (1)	28 (0.5)	28.3 (0.4)
Median (range)	27.6 (14.7-48.3)	26.8 (18.5-52.1)	26.9 (14.7-52.1)
Distribution – no. (%)
<30	35 (64.8)	124 (70.9)	159 (69.4)
≥30	19 (35.2)	51 (29.1)	70 (30.6)
P-value	0.399
Stage at the diagnosis – no. (%)
Stage I	2 (7.4)	26 (3.3)	28 (3.4)
Stage II	16 (59.3)	111 (14)	127 (15.5)
Stage III	9 (33.3)	180 (22.8)	189 (23.1)
Stage IV	0 (0)	474 (59.9)	474 (57.9)
P-value	<0.001
Tumour mutation burden
Mean (St. Err)	6.8 (0.6)	5.1 (0.2)	5.3 (0.2)
Median (range)	4 (0-51)	3 (0-49)	4 (0-51)
Distribution – no. (%)
≤ 5	81 (56.6)	806 (71.4)	887 (69.7)
6 – 10	32 (22.4)	183 (16.2)	215 (16.9)
> 10	30 (21)	140 (12.4)	170 (13.4)
P-value	0.001
Statistically significant P-value (<0.05). Chi-square test was used to measure the p-value. Abbreviations: St. Err – standard error of the mean; yrs. – years; BMI – body mass index.

Table 8 Characteristics of patients with FBXW7 alterations and wild-type
Characteristic	Altered (n=131)	Wild-type (n=1141)	Total (n=1272)
Age
Mean (St. Err)	63.8 (1.3)	59.8 (0.4)	60.2 (0.4)
Median (range) – yrs.	64 (20-90)	60 (13-93)	60 (13-93)
Distribution – no. (%)
<40	8 (6.2)	85 (7.5)	93 (7.3)
40-59	41 (31.5)	469 (41.1)	510 (40.2)
60-79	58 (44.6)	493 (43.2)	551 (43.4)
≥80	23 (17.7)	93 (8.2)	116 (9.1)
P-value	0.002
Sex – no. (%)
Male	73 (55.7)	589 (51.6)	662 (52)
Female	58 (44.3)	552 (48.4)	610 (48)
P-value	0.373
BMI – Kg/m²
Mean (St. Err)	25 (4.1)	28.3 (0.4)	28.3 (0.4)
Median (range)	21.6 (19.1-41.2)	27.1 (14.7-52.1)	26.9 (14.7-52.1)
Distribution – no. (%)
<30	4 (80)	155 (69.2)	159 (69.4)
≥30	1 (20)	69 (30.8)	70 (30.6)
P-value	0.604
Stage at the diagnosis – no. (%)
Stage I	3 (3)	25 (3.5)	28 (3.4)
Stage II	26 (26.3)	101 (14)	127 (15.5)
Stage III	27 (27.3)	162 (22.5)	189 (23.1)
Stage IV	43 (43.4)	431 (59.9)	474 (57.9)
P-value	0.004
Tumour mutation burden
Mean (St. Err)	11.9 (0.8)	4.5 (0.2)	5.3 (0.2)
Median (range)	9 (1-51)	3 (0-41)	4 (0-51)
Distribution – no. (%)
≤ 5	37 (28.2)	850 (74.5)	887 (69.7)
6 – 10	32 (24.4)	183 (16)	215 (16.9)
> 10	62 (47.3)	108 (9.5)	170 (13.4)
P-value	<0.001
Statistically significant P-value (<0.05). Chi-square test was used to measure the p-value. Abbreviations: St. Err – standard error of the mean; yrs. – years; BMI – body mass index.

Table 9 Summary of current treatment evidence for targeted genes
Gene	Model Type	TREATMENT	Preclinical/Clinical Evidence
*APC*	Mouse xenograft model for colorectal cancer	RK‐287107, a tankyrase inhibitors	Resulted in an approximate 50% reduction in relative tumour volume in an APC-mutated cancer model [88].
*TP53*	Mouse xenograft model for multiple myeloma	Bispecific antibody to p53 R175H-HLA complex	Resulted in regression of tumours possessing R175H p53 mutation [86].
*BRAF*	Human colorectal cancer phase 3 trial	Encorafenib, cetuximab, and binimetinib	Resulted in improved median overall survival (9.0 vs 5.4 months) in metastatic colorectal cancer patients with the BRAF V600E mutation [89].
*RBFOX1*			No clear evidence of treatment association.
*FBXW7*			No clear evidence of treatment association.
*PTPRT*	Retrospective analysis of metastatic colorectal cancer patients	Bevacizumab	A higher rate of non-response was observed in patients with PTPRT mutations [90].
*TTN*	Retrospective analysis of advanced non-small cell lung cancer patients	Immune checkpoint blockade therapy	Resulted in an objective response in 46.2% of patients in patients with altered TTN, compared to just 21.2% in TTN wild-type patients [52].
*CSMD1*			No clear evidence of treatment association.
*WWOX*			No clear evidence of treatment association.
*MACROD2*			No clear evidence of treatment association.
*SRC*	Human colorectal cancer phase I trial	Dasatinib, capecitabine, oxaliplatin, and bevacizumab	Resulted in an overall response rate of 75% in patients with high activated SRC expression [91].
*SYNE1*	Retrospective analysis of clear cell renal cell carcinoma patients	Immune checkpoint blockade therapy	A better response to therapy was observed in patients with SYNE1 mutation [92].
The table contains key genes associated with altered outcome in colorectal cancer and current evidence on treatment implications where available. The table details both preclinical and clinical evidence in addition to the experimental model type used, summarising the key data. Abbreviations: APC – Adenomatous Polyposis Coli gene; BRAF – B-Raf Proto-Oncogene; CSMD1 – CUB And Sushi Multiple Domains 1 gene; FBXW7 – F-Box And WD Repeat Domain Containing 7 gene; HLA – Human Leukocyte Antigen; MACROD2 – Mono-ADP Ribosylhydrolase 2 gene; PTPRT – Protein Tyrosine Phosphatase Receptor Type T gene; RBFOX1 – RNA Binding Fox-1 Homolog 1 gene; SRC – SRC Proto-Oncogene, Non-Receptor Tyrosine Kinase; SYNE1 – Spectrin Repeat Containing Nuclear Envelope Protein 1 gene; TP53 – Tumour Protein p53 gene; TTN – Titin gene; WWOX – WW Domain Containing Oxidoreductase gene.

Table 10 Summary of current treatment evidence for targeted genes that did not reach significance
Gene	Model Type	TREATMENT	Preclinical / Clinical Evidence
*KRAS*	Current clinical practice in colorectal cancer patients	Cetuximab	Resulted in improved overall survival (9.5 vs. 4.8 months) in KRAS wild-type cancer patients [93].
*PIK3CA*	Human breast cancer phase III trial	Fulvestrant and alpelisib	Resulted in improved progression free survival (11.0 vs 5.7 months) in patients with PIK3CA mutation [94].
*SMAD4*	Human colorectal cancer cell line	Cetuximab	SMAD4 silencing reduced cell sensitivity to cetuximab as demonstrated by cell viabilty assays. There was also an increase in cell models of migration and invasion [95].
The table contains key genes that were not associated with altered outcome in colorectal cancer and current evidence on treatment implications where available. The table details both preclinical and clinical evidence in addition to the experimental model type used, whilst summarising the key data. Abbreviations: KRAS – Kirsten Rat Sarcoma Viral oncogene; PIK3CA – Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha; SMAD4 – SMAD Family Member 4.

No competing interests reported.

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The prognostic value of gene alterations in colon cancer patients: An analysis of the cBioPortal database

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