Functional validation of somatic variability in TP53 and KRAS for prediction of platinum sensitivity and prognosis in epithelial ovarian carcinoma patients

doi:10.21203/rs.3.rs-5224537/v1

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Functional validation of somatic variability in TP53 and KRAS for prediction of platinum sensitivity and prognosis in epithelial ovarian carcinoma patients

https://doi.org/10.21203/rs.3.rs-5224537/v1

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Background: Concerning the dismal prognosis of chemoresistant patients with epithelial ovarian carcinoma (EOC), we aimed to validate the findings of a previous whole exome sequencing study on 50 patients using an orthogonal Sanger sequencing method on the same patients and a separate set of 127 EOC patients (N=177).

Methods: We focused on TP53 as a frequently mutated gene relevant for chemosensitivity, included KRAS as an additional therapeutically relevant target, complemented study with transcript levels of both genes, and compared results with clinical parameters.

Results: All variants in TP53 and KRAS detected by exome sequencing were confirmed. KRAS mutated patients had significantly more frequently FIGO stages I or II (p=0.007) and other than high-grade serous tumor subtypes (nonHGSCs) (p<0.001), which was connected with lower KRAS transcript levels (p=0.004). Patients with nonHGSCs harboring TP53 missense variants disrupting the DNA binding loop had significantly poorer platinum-free interval than the rest (p=0.008). Tumors bearing nonsense, frameshift, or splice site TP53 variants had a significantly lower TP53 transcript level, while those with missense variants had significantly higher levels than wild-types (p<0.001). The normalized intratumoral TP53 and KRAS transcript levels were correlated, and three patients with both genes co-mutated had extremely poor survival.

Conclusions: Our study points to KRAS as a target for future therapy of nonHGSCs and reveals the prognostic value of TP53variants in the DNA binding loop.

epithelial ovarian carcinoma

platinum sensitivity

TP53

KRAS

variant

transcript expression

Epithelial ovarian cancer (EOC), recognized as the eighth leading cause of cancer-related death among women, stands out as one of the most lethal gynecological malignancies¹. Early detection of EOC is a challenge, because, in most cases, it is asymptomatic in the early stages (I or II based on The International Federation of Gynecology and Obstetrics, FIGO, guidelines). Typically, 75% of EOC cases occur at the advanced stage (III or IV), where the 5-year survival rate is approximately 20–45%, compared to 40–70% for stages I or II^2,3. The standard treatment for advanced EOC has been primary debulking surgery followed by chemotherapy (platinum derivatives with paclitaxel) for the majority of cases⁴. However, most patients experience a relapse within the first five years after the initial diagnosis, with only 20–25% achieving cure⁵.

Morphologically, EOCs are classified into four major subtypes: serous, endometrioid, clear cell, and mucinous⁶. Additionally, they can be divided into two primary types: type I, including endometrioid, mucinous, clear cell, and low-grade serous ovarian carcinomas (LGSCs), and type II, constituting 70% of the total and encompassing high-grade serous ovarian carcinomas (HGSCs), carcinosarcomas, and undifferentiated carcinomas. These classifications are integral to defining the aggressiveness of the cancer and its response to different chemotherapies³. A significant majority, exceeding 80% of identified EOC cases, fall under the histological classification of HGSC, characterized by an aggressive phenotype that correlates with elevated mortality rate⁷, which is attributed not only to diagnosis at the advanced stage but also to chemoresistance, where approximately 50% of the cases diagnosed at advanced stage relapse within the first five years³.

The introduction of Poly(ADP-ribose) polymerase (PARP) inhibitors like olaparib and antiangiogenic agents, such as bevacizumab or pazopanib, has led to a significant improvement in the prognosis of the patients^8,9. PARP inhibitors (PARPis) are primarily used for maintenance therapy for platinum-sensitive advanced EOCs¹⁰. Moreover, patients with BRCA1/BRCA2 mutations demonstrate enhanced sensitivity to treatment with PARPi¹¹.

Next Generation Sequencing (NGS) enables the detection of genetic variability and its linkage to multidrug resistance. Based on genomic profiling, two major EOC types have been defined. EOC of type I is characterized by mutations in the MAPK pathway (KRAS, BRAF, PTEN, and CTNNB1, etc.) and type II mutations in TP53, BRCA1, BRCA2, KIT, and EGFR¹². Moreover, DNA damage response and related alterations in DNA repair pathways play a crucial role in cancer development, including EOC. Germline mutations in DNA repair genes can predict hereditary forms of cancer, particularly BRCA1/2 mutations in breast and ovarian cancers¹⁴. Pathogenic somatic mutations in genes from the homologous recombination DNA repair pathway, such as BRCA1/2, ATM, RAD51C, and RAD51D, were implicated in chemosensitivity and prognosis of EOC patients^15,16. HGSC typically shows very high frequency of somatic TP53 mutations (~ 90%) and genomic heterogeneity¹³.

Our study aimed to validate the results of a previous whole exome sequencing study of 50 patients¹⁵, which confirmed TP53 as the most frequently mutated gene in HGSC and EOC in general and suggested its relevance for chemosensitivity. Using direct Sanger sequencing of the same samples, we demonstrate the robustness of mutation detection and provide validation study results using an extended sample set analyzed by the same method. We also include KRAS as an additional target of interest and complement somatic mutation screening with an assessment of both genes’ transcript levels in tumor RNA. We compare the results with sensitivity to EOC therapy and patient survival for evaluation of the prognostic value of these biomarkers. Our study adds another dimension to exome or genome sequencing-based EOC projects published before^15,17–19.

Patients

For this study, we used samples of surgically resected, primary EOC tumors from 50 patients (confirmation set) with available whole exome data¹⁵ and additional 127 EOC patients (validation set) without exome data. Patients were prospectively recruited at University Hospitals Motol, Královské Vinohrady (both in Prague, Czech Republic), and Pilsen (Czech Republic) between 2009 and 2020. Tumor samples were collected fresh and promptly frozen and stored at -80°C until isolation of nucleic acids. Peripheral blood samples were taken from all patients to enable tumor-normal matched analysis.

Collaborating clinicians collected the following clinical data on each patient: age at diagnosis, FIGO stage (pTNM), the histological subtype and grade of the tumor, presence of distant metastasis or residuum after surgery, oncological treatments, chemosensitivity status, and overall survival (OS) from medical records. The chemosensitivity status was based on the platinum-free interval (PFI) measured as the time from the end of the platinum-based adjuvant chemotherapy to disease recurrence or progression²⁰. Patients having PFI ≤ 6 months were considered platinum-resistant and patients with PFI ≥ 12 months platinum-sensitive. Several patients had PFI in the range of 7–12 months and were classified as partially platinum-sensitive²¹. These patients were tentatively included in the resistant group and all association analyses were performed both with and without them. Consensual results are provided. The OS was defined as the time elapsed between surgical resection and death of any cause or patient censoring. Detailed clinical characteristics of the patients are in Table 1.

Table 1

Clinical characteristics of EOC patients
Parameters	Number of patients	Percentage
Age at diagnosis	177	100
Median ± SD (years)	62.0 ± 11.5
FIGO stage
I	12	7
II	10	6
III	139	82
IV	8	5
Data not available	8	̶̶
Histologic grade (G)
G1	11	6
G2	15	9
G3	146	85
Gx	5	̶̶
Tumor subtype
HGSC	143	84
Other*	28	16
Data not available	6	̶̶
Distant metastasis
Absent	161	95
Present	8	5
Data not available	8	̶̶
Neoadjuvant chemotherapy
Administered	57	32
Not administered	120	68
Residuum after surgery
Present**	85	50
Absent (R0)	85	50
Data not available	7	̶̶
Adjuvant chemotherapy
Platinum-based^#	165	96
Taxane monotherapy	2	1
Not administered	4	3
Data not available	6	̶̶
Chemosensitivity status
Resistant (PFI ≤ 6 months)	38	23
Intermediate (PFI 7–11 months)	24	15
Sensitive (PFI ≥ 12 months)	101	62
Data not available	14	̶̶
Platinum-free interval	168	95
Median ± 95% confidence interval (months)	25	17.9–32.1
Overall survival	168	95
Median ± 95% confidence interval (months)	48	37.7–58.3
Footnotes:

*Other subtypes include the following carcinomas: mucinous (n = 9), clear cell (n = 10), low grade serous (n = 5), endometrioid (n = 2), and borderline (n = 2).

**Includes all ratings above R0 (R1, R2, unspecified).

^#Platinum-based chemotherapy regimens include n = 147 taxane (paclitaxel/docetaxel) with platinum (carboplatin/cisplatin), n = 9 platinum monotherapy, other (n = 1 FOLFOX, n = 1 platinum with anthracycline, n = 1 platinum with paclitaxel and anthracycline, and n = 6 platinum with paclitaxel and cyclophosphamide). FOLFOX = 5-fluorouracil, leucovorin, and oxaliplatin.

Experimental protocol of the study was approved by the Institutional Review Boards of the National Institute of Public Health in Prague (approval reference no. IGA NS9803-4 of 2 February 2008), University Hospital Motol (approval reference no. EK-890/15 of 24 June 2015), University Hospital Královské Vinohrady (approval reference no. EK-VP/40/0/2017 of 28 June 2017), and University Hospital Pilsen (approval reference no. 16-29013A of 4 June 2015). All patients included in the study read and signed the Informed Consent of the Patient.

Isolation of nucleic acids and cDNA synthesis

DNA from peripheral blood lymphocytes was isolated using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany). Processing tumor tissue samples involved grinding them into a fine powder using a mortar and pestle under liquid nitrogen. Subsequently, we utilized the AllPrep DNA/RNA/Protein Mini Kit (Qiagen) according to the manufacturer's protocol for the isolation of total RNA and DNA. The quantity of the RNA and DNA samples was assessed using the Qubit 4 Nucleic Acid Fluorometric Quantification System (ThermoFisher Scientific, Waltham, MA, USA) and quality was checked by measuring the integrity number (RIN and DIN) using Agilent TapeStation 2200 (ThermoFisher Scientific). RNA was transcribed into cDNA with the help of the RevertAid™ First Strand cDNA Synthesis kit (ThermoFisher Scientific) according to the manufacturer's protocol and checked using the previously published method²¹.

Quantitative Polymerase Chain Reaction

Quantitative real-time PCR (qPCR) was performed using TaqMan® Gene Expression Assays (ThermoFisher), namely TP53 (Hs01034249_m1) and KRAS (Hs00364284_g1). PPIA (Hs99999904_m1), UBC (Hs00824723_m1), and YWHAZ (Hs03044281_g1), selected previously using NormFinder and geNorm software, served as reference genes for results normalization²². The reaction mixture with volume of 5 µL contained 1 µL of 5× Hot FirePol Probe qPCR Mix Plus (ROX) (Solis BioDyne OÜ, Tartu, Estonia), 0.25 µL of 20× TaqMan® Gene Expression Assay specified above, 1.75 µL of nuclease-free water, and 2 µL of 8-times diluted cDNA. qPCR reactions were performed in a 384-well block of the ViiA7 Real-Time PCR System and evaluated using the ViiA7 System Software (Life Technologies, Carlsbad, CA, USA). Cycling parameters were initially held at 50 ◦C for 2 min and 10 min denaturation at 95 ◦C, followed by 45 cycles consisting of 15 sec of denaturation at 95 ◦C and 60 sec of annealing/extension at 60 ◦C. The non-template control contained water instead of cDNA and negative cDNA synthesis controls (RNA transcribed without reverse transcriptase) were employed to control carry-over contamination. All samples were analyzed in duplicates and samples with a standard deviation > 0.5 Ct between replicates were re-analyzed. The qPCR process adhered to the Minimum Information for Publication of Quantitative Real-Time PCR Experiments Guidelines (MIQE)²³.

Differences between samples and groups of patients were calculated from raw Ct values with the comparative Ct method described previously²⁴. The 2^−∆Ct method was used for relative quantification of gene expression, and the 2^−∆∆Ct method was used for fold change calculation in groups divided by differences in sensitivity to therapy or mutation classification.

Direct sequencing

Exons 2 and 3 of KRAS and 5–10 of TP53 were subjected to direct sequencing using the Sanger method. Briefly, DNA was amplified between oligonucleotide primer pairs specific for each amplicon (Table S1) using regular PCR and after product length verification on agarose gel purified by ethanol precipitation. Each reaction was optimized to produce a strong single-band product. Sequencing reactions were then performed using the BigDye Terminator v3.1 Cycle Sequencing Kit (Invitrogen) with approximately 10 ng of PCR product and 2 pmol of sequencing primer in 10 µl final reaction volume according to the producer’s protocol. Separate sequencing reactions were run with both forward and reverse sequencing primers (Supplementary Table S1). The acquired products were purified using ExoSAP-IT™ PCR Product Cleanup Reagent (Applied Biosystems, Foster City, CA). DNA sequencing was performed by a capillary electrophoresis-based system commercially (SEQme, s.r.o., Dobris, Czech Republic). Raw results were evaluated by BioEdit 7.2.5 program and Sequencing Analysis Software v5.2 (Applied Biosystems).

External datasets

For validation of somatic variants in TP53 and KRAS, the American Association for Cancer Research (AACR) Genomics Evidence Neoplasia Information Exchange (GENIE) 15.0-public release dataset (released on 1 Feb, 2024), composed of tumor panel sequencing data from multiple major cancer centers, was utilized²⁵. Only samples fulfilling the following criteria were used: EOC, primary tumor, any somatic mutation data found after matching by sample ID, and gene of interest (TP53 and/or KRAS) included in the respective sequencing panel (final dataset: n = 2210). For validation of expression levels and mutation data, we used the RNAseq gene expression (FPKM-UQ normalized) and DNAseq mutation data of the GDC TCGA-OV cohort (Mutect2 pipeline), downloaded from the University of California Santa Cruz Xenabrowser portal (https://xenabrowser.net), which were then filtered to only primary ovarian tumors (n = 374). The dataset does not contain minority subtypes, nor detailed histopathological annotation, with all samples being classified merely as serous, and therefore, subtype-sensitive analyses were not performed using this dataset.

Statistical analysis

Associations of categorical clinical data of patients (stage, grade of tumor, residuum, chemosensitivity status) with functional classification of mutations were analyzed using the Pearson chi-square or Fisher’s exact test. For the evaluation of associations of continuous variables such as age at diagnosis or transcript expression with categorical ones, the Kruskal-Wallis test was used. Correlations among continuous variables were tested with Spearman’s rho correlation. All tests were two-sided and p-values < 0.05 were considered statistically significant. Survival curves were plotted using the Kaplan-Meier method. Expression levels were distributed by quartiles and the “optimal cut-off” was defined as the highest statistical significance by the log-rank test. All statistical analyses were performed using the SPSS v16 program (SPSS, Chicago, IL, USA).

Patients’ characteristics

The main characteristics of all patients (N = 177) are in Table 1. The median age of patients at the time of diagnosis was 62 years (range 24–89). Most patients presented with FIGO stage III (82%), grade G3 (85%), and HGSC subtype (84%). About one-third of patients (32%) underwent preoperative chemotherapy, and half of patients (50%) had disease residuum left after surgical tumor debulking. The vast majority of patients (96%) received platinum-based chemotherapy regimens in an adjuvant setting, two received taxane monotherapy, four did not receive any adjuvant treatment due to poor performance status, and for six patients the information about therapy was not available. The median PFI and OS were 25 and 48 months, respectively. Patients with FIGO stage III or IV, residuum after surgery (R1 or R2), or with PFI < 12 months had significantly poorer OS than the rest of the patients (p < 0.001 for all) (Supplementary Fig. S1A-C).

Somatic genetic variability

All six KRAS variants found previously by exome sequencing (n = 50) were also detected by Sanger sequencing in the confirmation part of the study (n = 50). In the extended validation part (n = 125, two samples not assessed due to the lack of DNA), variants in a further six samples were observed (Table 2).

Table 2

Molecular characteristics of EOC patients
Gene	Number of patients	Percentage
KRAS mutation status*
KRAS wild-type	163	93
KRAS mutated	12	7
KRAS mutation spectrum
p.Gly12Asp	5	42
p.Gly12Val	3	26
p.Gly12Cys	1	8
p.Gly12Ala	1	8
p.Gln61Arg	1	8
p.Gln61His	1	8
TP53 mutation status
TP53 wild-type	92	52
TP53 mutated	85	48
TP53 mutation spectrum
Hotspots
p.Arg175His	8
p.Arg273His	5
p.Arg248Gln	5
p.His214Arg	3
p.Tyr220Cys	3
p.His179Gln	2
p.Arg248Trp	2
p.Asp259Tyr	2
p.Arg282Trp	2
p.Glu198Ter	3
p.Arg213Ter	3
Private missense mutations	27
Private frameshift or nonsense mutations	13
Private splice site mutations with pathogenic features	4
TP53 mutation functional consequences^#
Loss-of-function	37	56
Gain-of-function	29	44
Not classified	19	̶̶
Dominant-negative effect (DNE) & loss-of-function (LOF) properties^#
DNE_LOF	54	83
notDNE_notLOF	6	9
notDNE_LOF	5	8
Not classified	20	̶̶
Transactivation function^#
non-functional	51	34
functional or partially functional	97	66
Not classified	29	̶̶
DNA binding loop affected^#
yes	43	24
no	134	76
Footnotes:

*Result for two samples not available due to DNA of low quality/quantity.

^#Evaluated using The TP53 database of NCI (https://tp53.isb-cgc.org/) and The Clinical Knowledgebase (CKB) powered by The Jackson Laboratory database (https://ckb.jax.org/) and literature cited therein.

All variants were missense single nucleotide substitutions in exon 2 (n = 10) or 3 (n = 2). Representative chromatograms are in Supplementary Fig. S2A, B.

As for TP53, the confirmation set showed exactly the same variants compared to exome sequencing, i.e., 39 mutated and 10 wild-type patients, except in one sample where originally the variant p.Pro75fs was detected in exon 4, but the exon was then not covered by the direct sequencing approach. In the validation set (n = 127), an additional 45 mutated samples were identified (Table 2). Representative chromatograms are presented in Supplementary Fig. S3A-J.

Functional classifications enabled the distribution of TP53 variants to several categories: i/ missense (n = 62), out of which 27 were single private mutations and the rest mutational hotspots detected in two or more patients, ii/ two hotspot nonsense variants present in three patients each, and iii/ private frameshifts or nonsense variants (n = 13). The last category was splice site variants with pathogenic features, which were all private (n = 4). The TP53 database of NCI and The Clinical Knowledgebase (CKB) enabled more detailed stratification of variants into loss-of-function (n = 37) versus gain-of-function (n = 29) variants. Most of the somatic variants were classified as having the following properties: dominant-negative effect or loss-of-function (n = 54), non-functional transactivation (n = 51), and affecting DNA binding loop (n = 43) by these databases (Table 2).

Three patients carried mutations in both TP53 and KRAS (co-mutations). One patient with the HGSC subtype had the combination of TP53-Arg282Trp with KRAS-Gln61His, chemoresistant status, and OS of 16 months. The second patient had a clear cell subtype, TP53-Arg248Gln with KRAS-Gln61Arg, chemoresistant status, and extremely short OS of 7 months. The third patient with the mucinous subtype had TP53-Arg213Ter with KRAS-Gly12Asp mutation combination, chemoresistant status, and OS of 19 months. These three available cases suggest that carriage of TP53-KRAS co-mutations could be associated with chemoresistance and poor patient prognosis.

All subsequent clinical genomic analyses were performed using the combined confirmation and validation cohorts (N = 177).

Intratumoral KRAS and TP53 transcript levels

To provide additional functional evidence, we analyzed by qPCR the TP53 and KRAS transcript levels in all available tumor samples together with genetic information. Five samples could not be determined due to low RNA quantity or quality and no tissue left. No extreme outliers were observed.

The carriage or type of KRAS mutations did not significantly associate with the KRAS transcript level (p > 0.05). On the other hand, a significantly lower TP53 transcript level in tumors bearing nonsense, frameshift, or splice site types of variants compared to wild-type TP53 was observed (p < 0.001, Fig. 1A). In contrast, tumors with missense TP53 variants had significantly higher transcript levels than wild-type ones (p < 0.001, Fig. 1A). Higher TP53 transcript level was found in tumors with TP53 variants classified as gain-of-function compared to loss-of-function (p = 0.018), non-functional vs. functional transactivation (p = 0.001), or DNA binding loop affecting vs. other (p < 0.001) (Supplementary Fig. S4A-C).

Carriage of co-mutated TP53-KRAS did not affect transcript expression (p = 0.224 for TP53 and p = 0.204 for KRAS).

Interestingly, the normalized intratumoral TP53 and KRAS transcript levels were mutually significantly correlated (ρ = 0.384, p < 0.001, Fig. 1B).

Figure 1

Associations of TP53 and KRAS normalized transcript levels in tumors with characteristics of EOC patients

(A) TP53 normalized transcript level with TP53 mutation type. (B) Mutual correlation between KRAS and TP53 transcript levels. (C) KRAS normalized transcript level with EOC subtype.

HIGH means nonsense, frameshift, or splice site functional variant classification.

Associations of somatic genetic variability and transcript levels with clinical data of patients

Afterward, we performed statistical analysis of associations between transcript levels, mutational status, spectra, and functional classifications of both genes and clinical data of patients.

Patients with FIGO stage I or II had significantly more frequently mutated KRAS compared to stage III or IV patients (p = 0.007, Table 3). On the other hand, patients with the HGSC tumor subtype had significantly less frequently mutated KRAS (p < 0.001, Table 3), and they had significantly higher KRAS transcript levels (p = 0.004, Fig. 1C) compared to those with other EOC subtypes. KRAS mutation status, spectra, or transcript level were not significantly associated with the rest of the clinical parameters (age, grade of tumor, surgical radicality, chemosensitivity status, or OS, all p > 0.05), and this was true for the association between transcript level and stage as well. Patients with nonHGSC subtypes had significantly more often less advanced stages I/II than HGSCs (p < 0.001, Supplementary Table S2) and thus less aggressive disease. However, only the PFI of patients with clear cell subtype (n = 10) was any better than that of HGSC (n = 134), while for mucinous (n = 9) or LGSC (n = 5), it was not, and endometrioid patients had worst PFI (n = 2) (Supplementary Fig. S5A). However, for OS the difference between subtypes was not that apparent (Supplementary Fig. S5B).As for TP53, its transcript level, mutation status, spectra, or functional classifications were not significantly associated with any of the clinical parameters (age, stage, grade of tumor, subtype, surgical radicality, chemosensitivity status, or OS, all p > 0.05).

Table 3

Associations between *KRAS* mutational status and stage or tumor subtype of EOC patients
Characteristics	KRAS wild-type*	KRAS mutated*	p-value
Stage I/II	17	5	0.007
Stage III/IV	139	6
HGSC	140	2	< 0.001
other subtypes	19	9
Footnotes:

*Numbers of patients; for some patients clinical data or KRAS mutation status were not available.

We further performed patient stratification into HGSC and nonHGSC subgroups, given the importance of the EOC subtype in previous analyses. No significant associations with clinical data were identified for KRAS or TP53 transcript levels, mutations, or their functional classifications in the HGSC subgroup (n = 143). However, patients with nonHGSC subtypes (n = 28) bearing any TP53 mutations had non-significantly poorer PFI than patients with the wild-type (p = 0.062, Supplementary Fig. S6), and patients with TP53 missense variants disrupting the DNA binding loop had significantly poorer PFI than patients without these alterations, including wild-type carriers (p = 0.011, Fig. 2A). No association was found for OS or other clinical data, including chemosensitivity status.

Patients with co-mutated TP53-KRAS had significantly worse PFI and OS than wild-type patients or those with a single gene mutation (p < 0.001 for both, Fig. 2B, C).

Figure 2

Associations between patient survival and carriage of TP53 or KRAS mutations

(A) Platinum-free interval stratified by carriage of TP53 DNA binding domain mutations in EOC patients with nonHGSC subtype. (B) Platinum-free interval and (C) overall survival in TP53-KRAS co-mutated patients compared to wild-type or single gene mutated EOC patients of all subtypes.

Validation using external datasets

Finally, we attempted to validate our findings using the largest and most up-to-date publicly available EOC dataset within the GENIE project (n = 2210).

The TP53 and KRAS mutation analysis confirmed the overrepresentation of KRAS mutations in nonHGSC compared to HGSC cases. In our dataset, 32% of nonHGSC patients harbored KRAS mutations, while only 1.4% of HGSC cases had such alterations (Table 3). Size of the GENIE dataset allowed the analysis of the distribution of KRAS mutations across all major nonHGSC subtypes. The frequency of KRAS mutations raised in the trend HGSC (1.2%) < < clear cell (13%) < endometrioid (27%) = LGSC (28%) < mucinous (67%). Even more interesting was the trend in the ratio of TP53/KRAS mutability among subtypes, where mucinous and clear cell cases had a 1/1 ratio, while endometrioid and LGSC subtypes had more KRAS than TP53 mutations. Patients with the HGSC subtype had a ratio close to 100/1 in favor of TP53. Most interestingly, analysis of the GENIE dataset revealed a considerable fraction of TP53-KRAS co-mutated patients, again with a high heterogeneity across subtypes. Almost half (46%) of patients with the mucinous subtype had both genes mutated. The other subtypes had a much lower proportion of such events, 4% for endometrioid and 1.5% for clear cell EOC. The occurrence of this phenomenon in LGSC and HGSC was comparable and less than 1% in both cases (Fig. 3A).

As GENIE does not contain expression data, we used the TCGA-OV dataset (n = 374) for the assessment of transcript levels. The comparison of TP53 transcript levels with main mutation classification groups confirmed the trend observed in our study, i.e., significantly higher level in tumors harboring missense mutations (p = 0.002) and lower in those with nonsense, frameshift, or splicing mutations of (p = 0.009) compared to wild-type (Fig. 3B). For KRAS, no significant association of transcript expression with mutation spectra was found (p > 0.05, Fig. 3C), perhaps due to the low number of observations (n = 3 mutated samples). A weak, non-significant correlation was observed between TP53 and KRAS transcripts (p = 0.052, Fig. 3D). In terms of available clinical data, neither grade (G1 or G2 versus G3 or G4) nor stage (stage I or II versus III or IV) were significantly associated with KRAS or TP53 transcript level (p > 0.05, data not shown).

Only two KRAS-TP53 co-mutations were found in the TCGA dataset. One patient (TCGA-29-1696-01A) had KRAS Gly12Arg with TP53 frameshift co-mutation, stage IIIC, G2, and died 34 months after diagnosis. The second patient (TCGA-61-2009-01A) had KRAS Glu61Leu with TP53 missense co-mutation, stage IIIC, G3, and was alive 40 months after diagnosis. Thus, external data do not seem to corroborate our observation of the considerably poorer prognosis of the three EOC patients with such co-mutations.

Due to the absence of survival data in the public version of GENIE and the lack of histopathologically confirmed subtype stratification in the TCGA dataset (however, all tumors were serous), we could not attempt the validation of our prognostic associations (Fig. 2).

The present study confirmed by a gold standard direct sequencing method the presence of mutations in clinically actionable EOC oncodrivers TP53 and KRAS, reported by previous whole exome sequencing of 50 patients. We further screened both genes in the additional sample set of 127 EOC patients and complemented the somatic genotype with transcript expression data with the intent to provide deeper functional insight.

In general, results show that the functional classification of mutations and disease subtype context matters much more than carrier status alone. These aspects need careful investigation before any sensible clinical exploitation. Specifically, HGSC differs from other EOC subtypes by extreme dominance of TP53 mutations, while KRAS mutations are more relevant to nonHGSC subtypes. This observation agrees with the generally accepted view²⁶ and may have clinical consequences²⁷. The prevalence of KRAS mutations in patients with less advanced stages I/II found by us complies with the fact that some nonHGSC subtypes are more frequently diagnosed with less advanced disease than HGSC ones²⁸. Nevertheless, this was reflected by non-significantly better PFI only for clear cell subtype and just mild effect on general prognosis in terms of OS. However, the clear cell subtype is considered less sensitive to platinum-based chemotherapy than other EOCs²⁹. Thus, our observation may be due to the low number of samples evaluated (n = 10).

The striking prevalence of KRAS mutations in nonHGSC subtypes calls for their integration into clinical trials with future KRAS inhibitors. Studies with KRAS-G12D (MRTX1133)³⁰ or pan-KRAS³¹ inhibitors show promising results and the KRAS-G12C mutation inhibitors already have been approved for the personalization of therapy. Sotorasib has been approved for targeted therapy of non-small cell lung cancer (NSCLC)³² and adagrasib for NSCLC and colorectal carcinoma³³.

Subtype-sensitive analyses brought interesting results also for TP53 mutations classified by their location in the sequence. Those disrupting the DNA binding loop predisposed patients with nonHGSC subtypes to poorer PFI compared to patients with mutations not disrupting this domain. Such an association, to our best knowledge, has not been published yet. Although it concerns a small fraction of patients, it may provide predictive value for therapy targeted to specific TP53 mutations and/or function³⁴. The relevance of so-called loss-of-function or gain-of-function TP53 variants for disease progression and potential therapy targeting was discussed before³⁵. Therefore, we used The TP53 database of and The Clinical Knowledgebase for the stratification of patients into subgroups with variants classified according to different functional effects (Table 2). Nevertheless, except for the above-mentioned variants disrupting the DNA binding domain, no stratification had clinical consequences.

Perhaps the most important result for contemporary considerations on targeted therapy and immunotherapy appeared after the analysis of KRAS-TP53 co-mutated tumors. Previous studies in NSCLC^36,37 or pancreatic carcinoma³⁸ reported contradicting results. Despite both author groups demonstrating the dismal prognosis of patients harboring such alterations, the immunologically “hot” status has been claimed for NSCLC, while the “cold” status for pancreatic carcinoma with presumed consequences for the results of eventual immune checkpoint blockade therapy. Our study shows that EOC may be subject to further research in this area as all co-mutated patients in the present study (n = 3), no matter their subtype, proved to be chemoresistant and died very quickly, moving EOC closer in this to pancreatic cancer.

External validation using the GENIE dataset (n = 2210)²⁵ helped to validate the observed correlation between TP53 mutation types and transcript expression. More importantly, it enabled a more precise evaluation of the distribution of TP53 and KRAS mutation status across EOC subtypes. This analysis revealed an increasing trend in the ratio TP53/KRAS mutability: HGSC > > clear cell ≈ mucinous > endometrioid > > LGSC. An even more striking disproportion in TP53-KRAS co-mutation frequency: mucinous (53/115) > > endometrioid (9/223) > clear cell (4/265) > HGSC (14/1467) > LGSC (1/140) was apparent. Despite the common occurrence of co-mutations in the mucinous subtype was already described³⁹, both trends suggest enormous variability among EOC subtypes, which calls for exploitation in individualized therapy.

Several limitations of the present study need to be mentioned. Firstly, the sample size precludes robust analysis of nonHGSC subtypes, which are very rare (< 10% of EOC each). Unfortunately, due to serious constraints in clinical data availability in both GENIE (missing survival)²⁵ and TCGA (missing EOC subtype), we could not externally validate our potentially clinically relevant results. Thus, more studies are necessary for this area and our study may contribute to meta-analyses. Second, patients analyzed in this study were untreated with PARPi or other targeted drugs. Platinum-based chemotherapy is considered the standard of care in EOC⁴⁰, and chemosensitivity to platinum and PARPi overlaps^41,42. Thus, our sample set is still relevant from this point of view. Lastly, transcript levels may not robustly correlate with protein levels as assessed by immunohistochemistry. However, while immunohistochemistry is routinely clinically used for TP53 assessment, it is not in the case of KRAS, where just the mutation status is considered for EGFR blockade therapy, and functional approval, especially for rarely occurring variants, is missing. Our study shows that this area needs further attention. Finally, the present study has clear benefits in ethnical homogeneity of the patient population, unified therapy regimen, and long-term complete clinical follow-up.

In conclusion, our study confirms previous data on KRAS as a valid and hopefully soon druggable target for nonHGSC EOCs and identifies the prognostic value of TP53 mutations in the DNA binding loop for a fraction of patients. Furthermore, we describe an intriguing enrichment of TP53-KRAS co-mutations in the mucinous subtype of EOC based on analysis of an external dataset of 2210 samples. Our results further extend the area of precision oncology of EOC and suggest directions for future functional and preclinical studies.

AACR

The American Association for Cancer Research

CKB

The Clinical Knowledgebase

EOC

epithelial ovarian cancer

FIGO

The International Federation of Gynecology and Obstetrics

FOLFOX

5–fluorouracil, leucovorin, and oxaliplatin

GENIE

Genomics Evidence Neoplasia Information Exchange

HGSC

high–grade serous ovarian carcinomas

LGSC

low–grade serous ovarian carcinomas

MIQE

the Minimum Information for Publication of Quantitative Real–Time PCR Experiments Guidelines

NGS

next generation sequencing

nonHGSC

other than high–grade serous carcinoma

NSCLC

non–small cell lung cancer

overall survival

PARPi

poly(ADP–ribose) polymerase inhibitors

PFI

platinum–free interval

qPCR

quantitative real–time polymerase chain reaction

Ethics approval and consent to participate

Experimental protocol of the study was approved by the Institutional Review Boards of the National Institute of Public Health in Prague (approval reference no. IGA NS9803-4 of 2 February 2008), University Hospital Motol (approval reference no. EK-890/15 of 24 June 2015), University Hospital Kralovske Vinohrady (approval reference no. EK-VP/40/0/2017 of 28 June 2017), and University Hospital Pilsen (approval reference no. 16-29013A of 4 June 2015). All patients included in the study read and signed the Informed Consent of the Patient.

Consent for publication

Not applicable.

Data access

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Competing interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Funding

This work was funded by the Czech Health Research Council grant no. NU22-08-00186 to P.S.

Authors’ Contributions

Conceptualization – R.V. & P.S.

Methodology – M.A., E.A., I.K., V.H., & F.A.

Formal Analysis – P.H., R.V., & P.S.

Investigation – M.A., E.A., V.H., & I.K.

Resources – L.R., M.H., M.M., K.K., A.B., & J.B.

Writing – Original Draft – M.A., R.V., & P.S.

Writing – Review & Editing – M.A., E.A., I.K., P.H., V.H., F.A., L.R., M.H., M.M., K.K., A.B., J.B., R.V. & P.S.

Visualization – P.H & P.S.

Supervision – R.V. & P.S.

Project Administration – P.S.

Funding Acquisition – P.S.

Acknowledgements

The authors would like to thank all participating patients for their kind consent to the study and the clinical personnel for outstanding support.

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No competing interests reported.

AlObeedetalSupplementarymaterials.pdf
Supplementary materials Figure S1: Associations between stage (A), residuum after surgery (B), and chemosensitivity status (C) and overall survival of EOC patients Figure S2: Representative chromatograms of KRAS mutations assessed in EOC patients by direct Sanger sequencing A – codon 12 in exon 2, B – codon 61 in exon 3 Figure S3: Representative chromatograms of TP53mutations assessed in EOC patients by direct Sanger sequencing A – p.Arg175His, B – p.His179Gln, C – p.His214Arg, D – p.Tyr220Cys, E – p.Glu198Ter, F – p.Arg213Ter, G – p.Asp259Tyr, H – p.Arg273His, I – p.Arg282Trp, J – p.Arg248His/Trp. Figure S4: Differences in TP53 transcript levels between patients divided by functional classifications of TP53mutations Gain-of-function vs. loss-of-function (A), non-functional vs. functional transactivation (B), and DNA binding loop affecting vs. other (C) Figure S5: Association between subtype and platinum-free (A) and overall (B) survival of EOC patients Figure S6: Association between TP53mutation status and platinum-free interval of nonHGSC EOC patients Table S1: List of primers used for KRAS and TP53 mutation analysis in EOC patients by direct Sanger sequencing Table S2: Associations between EOC subtype and disease stage

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Functional validation of somatic variability in TP53 and KRAS for prediction of platinum sensitivity and prognosis in epithelial ovarian carcinoma patients

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Abstract

Figures

Introduction

Methods

Patients

Isolation of nucleic acids and cDNA synthesis

Quantitative Polymerase Chain Reaction

Direct sequencing

External datasets

Statistical analysis

Results

Patients’ characteristics

Somatic genetic variability

Intratumoral KRAS and TP53 transcript levels

Associations of somatic genetic variability and transcript levels with clinical data of patients

Validation using external datasets

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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