Combining imaging- and gene-based hypoxia biomarkers in cervical cancer improves prediction of chemoradiotherapy failure independent of intratumour heterogeneity

Abstract

Background: Emerging biomarkers from medical imaging or molecular characterization of tumour biopsies open up for combining the two and exploiting their synergy in treatment planning of cancer patients. We generated a paired data set of imaging- and gene-based hypoxia biomarkers in cervical cancer, appraised the influence of intratumour heterogeneity in patient classification, and investigated the benefit of combining the methodologies in prediction of chemoradiotherapy failure.

Methods: Hypoxic fraction from dynamic contrast enhanced (DCE)-MR images and an expression signature of six hypoxia-responsive genes were assessed as imaging- and gene-based biomarker, respectively in 118 patients.

Findings: Dichotomous biomarker cutoff to yield similar hypoxia status by imaging and genes was defined in 41 patients, and the association was validated in the remaining 77 patients. The two biomarkers classified 75% of 118 patients with the same hypoxia status, and inconsistent classification was not related to imaging-defined intratumour heterogeneity in hypoxia. Gene-based hypoxia was independent on tumour cell fraction in the biopsies and showed minor heterogeneity across multiple samples in 9 tumours. Combining imaging- and gene-based classification gave a significantly better prediction of PFS than one biomarker alone. A combined dichotomous biomarker optimized in 77 patients showed a large separation in PFS between more and less hypoxic tumours, and separated the remaining 41 patients with different PFS. The combined biomarker showed prognostic value together with tumour stage in multivariate analysis.

Interpretation: Combining imaging- and gene-based biomarkers may enable more precise and informative assessment of hypoxia-related chemoradiotherapy resistance in cervical cancer.

Funding: Norwegian Cancer Society, South-Eastern Norway Regional Health Authority, and Norwegian Research Council.

Keywords: Cervical cancer; Gene expression signature; Hypoxia; Intratumour heterogeneity; Medical imaging; Prognostic biomarker.

Fig. 1

Study design and hypoxia biomarkers. (a) Patients included at different stages of the study. Independent subgroups of the total cohort of 118 patients were used to determine biomarker cutoff for classification (n = 41) and to validate the association between the biomarkers and construct the combined biomarker (n = 77). The total cohort was used to assess the importance of intratumour heterogenity for biomarker performance and compare the combined biomarker with existing clinical markers. (b) Determination of the imaging-based biomarker from left to right, sagittal T₂W-image of the pelvic showing localization of image slices numbered from lower to upper part of the tumour, example of axial A_Brix-image of the tumour (slice 6) superimposed on T₂W-image, A_Brix-images of all slices covering the tumour, binary A_Brix-images of the same slices showing voxels in hypoxic and non-hypoxic regions according to an A_Brix threshold value of 1.56, and classification based on the hypoxic fraction of all slices combined. (c) Determination of the gene-based biomarker from left to right, sagittal T₂W-image of the pelvic showing localization of the region accessible for biopsies in the lower part of tumour, the approximate size of the sections from 1–4 biopsies (median 2) taken from each tumour and pooled for RNA isolation, expression data of 6 signature genes, and classification based on the signature value.

Fig. 2

Comparison of imaging-and gene-based hypoxia classification. Correlation plots of biomarker values, showing gene-defined versus imaging-defined hypoxia for 41 patients used in construction of the gene-based biomarker and to define cutoff of both biomarkers (a), 77 remaining patients (b) and all 118 patients (c). Dotted lines, classification cutoff of each biomarker, defining three groups according to the similarity in classification by imaging and genes. P-value and regression coefficient (rho) from Spearman correlation analysis are indicated. (d) Venn diagram showing the overlap of imaging- and gene-based classification of more and less hypoxic tumours for all 118 patients.

Fig. 3

Heterogeneity in hypoxia across image slices. (a) Tumour volume for the three classification groups defined in Fig. 2c. Jitter plot overlaid a boxplot, where the boxes extend from the first to third quartile with the median value indicated. P-values from Wilcoxon rank-sum test is indicated. (b) Binary images showing voxels in hypoxic and non-hypoxic regions of two patients with heterogeneous hypoxia status across image slices (left), and imaging-defined hypoxia (hypoxic fraction) of each slice (right). Dotted lines, classification cutoff (0.38). (c) Classification of individual image slices by the imaging-based biomarker. The patients are divided into three classification groups defined in Fig. 2c, and are sorted according to increasing biomarker value of the whole tumour for each group. (d) Correlation between imaging-defined hypoxia of the biopsy region and the whole tumour. Dotted line, 1:1 relationship; solid line, linear regression line. (e) Comparison of the imaging- and gene-based biomarker values using image slice 1 from the biopsy region. Dotted lines, classification cutoff of each biomarker. The three classification groups defined in Fig. 2c are indicated by colours. (f) Fraction of patients classified with same or different hypoxia status by imaging and genes, using images from the whole tumour or the biopsy region. Number of patients (n) and P-value from Fisher's exact test are indicated. (d, e) P-value and regression coefficient from Pearson (d) and Spearman (e) correlation analysis are indicated.

Fig. 4

Heterogeneity in hypoxia within biopsy region. (a) Determination of gene-based biomarker for multiple biopsies from 9 tumours. (b) Gene-based biomarker value for 2–4 biopsies from each of 9 tumours (24 samples). Dotted line, classification cutoff. (c) Binary image of the biopsy region for tumour 8 in (b). (d) Illustration of the simulation experiment. Sagittal T₂W-image of the pelvic with indication of selected image slice covering the biopsy region (left), binary image of selected slice showing voxels in hypoxic and non-hypoxic regions and location of 3 virtual samples, each of 12 voxels (middle), binary image and biomarker value (hypoxic fraction) of the 3 virtual samples (right). (e), Imaging-based biomarker value of the virtual samples for each patient sorted according to increasing biomarker value of the biopsy region. The boxes extend from the first to third quartile with the median value indicated. (f), Standard deviation (SD) of the imaging-based biomarker value of the virtual samples versus biomarker value of the biopsy region. Line, generalized additive model (GAM) fitted to the data to separate tumours with more (above the line) or less (below the line) clustering of hypoxic regions. Filled circles, tumours displayed in (g). (g) Binary images showing less or more clustering of hypoxic regions for 8 tumours indicated in (f). (h), Fraction of patients with less or more clustering of hypoxic regions for patients with same or different hypoxia status by imaging (biopsy region) and genes. Number of patients (n) and P-value from Fisher's exact test are indicated.

Fig. 5

Tumour cell fraction and gene-based classification. (a) Gene-based biomarker value versus tumour cell fraction in biopsy (n = 118). Mean fraction of multiple biopsies used for classification of the tumour is shown. The colour indicates the three classification groups defined in Fig. 2c. Dotted line, classification cutoff. P-value and regression coefficient (r) from Pearson correlation analysis are indicated. (b) Tumour cell fraction of the three classification groups. Jitter plot overlaid a boxplot, where the boxes extend from the first to third quartile with the median value indicated. P-value from Kruskal-Wallis test is indicated. (c) Tumour cell fraction and hypoxia status by the gene-based biomarker of individual biopsies from 9 tumours presented in Fig. 4b.

Fig. 6

Combined imaging- and gene-based biomarker. Kaplan-Meier curves for progression-free survival (PFS) of 77 patients with a more or less hypoxic tumour classified by imaging (a) or genes (b), and for the three classification groups defined in Fig. 2c (c). (d) Correlation plot of biomarker values for the same 77 patients, showing gene-defined versus imaging-defined hypoxia. The optimal line for classifying patients with a more (above the line) and less (below the line) hypoxic tumour to achieve the strongest association to PFS is shown. (e, f) Kaplan-Meier curves for PFS of patients classified into the two groups defined in (d) for the 77 patients used to define the line (e), and the 41 remaining patients (f). P-values from log-rank test, number of patients at risk, 60-month recurrence probability and HR with 95% CIs are indicated in the Kaplan-Meier plots.

Fig. 7

Prognostic value of hypoxia classification by the combined biomarker. Kaplan-Meier curves for progression-free survival (PFS) of 118 patients classified with a more or less hypoxic tumour (a) and for the same classification groups stratified for a low and a high tumour stage according to FIGO (b). P-values from log-rank test, number of patients at risk, 60-month recurrence probability and HR with 95% CIs (a) are indicated.