GIInger predicts homologous recombination deficiency and patient response to PARPi treatment from shallow genomic profiles

Abstract

Homologous recombination deficiency (HRD) is a predictive biomarker for poly(ADP-ribose) polymerase 1 inhibitor (PARPi) sensitivity. Routine HRD testing relies on identifying BRCA mutations, but additional HRD-positive patients can be identified by measuring genomic instability (GI), a consequence of HRD. However, the cost and complexity of available solutions hamper GI testing. We introduce a deep learning framework, GIInger, that identifies GI from HRD-induced scarring observed in low-pass whole-genome sequencing data. GIInger seamlessly integrates into standard BRCA testing workflows and yields reproducible results concordant with a reference method in a multisite study of 327 ovarian cancer samples. Applied to a BRCA wild-type enriched subgroup of 195 PAOLA-1 clinical trial patients, GIInger identified HRD-positive patients who experienced significantly extended progression-free survival when treated with PARPi. GIInger is, therefore, a cost-effective and easy-to-implement method for accurately stratifying patients with ovarian cancer for first-line PARPi treatment.

Keywords: HRD; PARPi; biomarker; breast cancer; cancer; convolutional neural network; homologous recombination deficiency; low-pass whole-genome sequencing; lpWGS; ovarian cancer.

Graphical abstract

Figure 1

GIInger predicts HRD status using spatially organized coverage profiles from lpWGS data (A) Study overview. (B) Example of GIInger input. Heatmap of the smoothed normalized coverage across ∼3 Mbp bins (columns) for all autosomes (rows) aligned with respect to their centromere (vertical dashed line). Bins are colored (blue-white-red scale) based on their normalized coverage relative to the mean coverage of the sample (set to 1, white). Color scale is depicted on the bottom. NaN (“not a number”) refers to non-existing relative chromosome locations. (C) GIInger architecture schematic. Input features are extracted through a series of convolution and pooling operations. The vector containing the 48 extracted features is provided to a set of fully connected layers trained to output the GIInger score, which predicts the sample’s genomic instability (GI) status based on the score threshold.

Figure 2

GIInger yields comparable results in predicting HRD status to tools that rely on high-coverage datasets (A) 101 breast cancer samples (x axis) and GIInger scores (y axis), ordered by GIInger score and colored according to their HRD status according to HRDetect as positive (blue) and negative (gray) and patterned according to their BRCA status as mutated (cross-hatching) and wild type (no hatching). (B) ROC curves for breast cancer HRD classification obtained using GIInger (red), LST (orange), GIS (green), LOH score (pink), and BRCA status (blue) using HRD status reported by HRDetect as reference. AUC values for each method are given within the insets.

Figure 3

GIInger analytical performance analysis in clinical samples (A) Representation of the multicenter study design. (B) Concordance of GI status between GIInger and the reference method (n = 296 samples). For details on rejection criteria, see STAR Methods. (C) GIInger scores (y axis) relative to the corresponding reference method GI score (x axis). Each point corresponds to a cancer patient sample (n = 125) and is labeled according to the GI status concordance between the two methods. Dashed lines indicate respective classification thresholds of GIInger and the reference method. Solid line indicates linear regression best fit; R² = 0.85.

Figure 4

GIInger clinical relevance analysis (A) Properties of samples included in the full PAOLA-1 cohort and the subset included in the present study. PFS curves stratified by treatment arms (orange: olaparib plus bevacizumab; gray: placebo plus bevacizumab) in the PAOLA-1 subcohort (n = 195) according to (B) GI-positive status assigned by GIInger (HR: 0.49, 95% CI: 0.28–0.85; p = 0.01) and (C) GI-negative status assigned by GIInger (HR: 0.82, 95% CI: 0.52–1.31; p = 0.41); y axis: probability of patients being free from disease progression and death; x axis: months since randomization; dashed lines indicate median survival times; and tables of absolute number of individuals at risk are given below each plot.

Figure 5

GIInger is a flexible and robust patient stratification method (A) Workflow schematics illustrating flexible use of GIInger in combined workflow with existing targeted sequencing approaches. Pre-capture library aliquots can be sequenced at ∼1× depth and inform sample HRD status together with data on BRCA status. (B) Qualitative workflow cost and complexity comparison of different methodologies for determining HRD-related biomarkers genome instability and BRCA status. CGP, comprehensive genomic profiling. Inferences are based on relative differences detailed in Table S3. (C) Concordance of GIInger scores between lpWGS-only (y axis) and combined (i.e., targeted sequencing with HRDv1 panel + lpWGS) workflow (x axis). Each point corresponds to a PAOLA-1 cancer patient sample (n = 124) and is labeled according to the GI status concordance between the two workflows. Dashed lines represent GIInger classification threshold, and solid line indicates linear regression best fit; R² = 0.99; overall concordance: 97.58% (95.31%, 99.55%).