Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types

Abstract

Background: Homologous recombination deficiency (HRD) is recognized as a pan-cancer predictive biomarker that potentially indicates who could benefit from treatment with PARP inhibitors (PARPi). Despite its clinical significance, HRD testing is highly complex. Here, we investigated in a proof-of-concept study whether Deep Learning (DL) can predict HRD status solely based on routine hematoxylin & eosin (H&E) histology images across nine different cancer types.

Methods: We developed a deep learning pipeline with attention-weighted multiple instance learning (attMIL) to predict HRD status from histology images. As part of our approach, we calculated a genomic scar HRD score by combining loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transitions (LST) from whole genome sequencing (WGS) data of n = 5209 patients across two independent cohorts. The model's effectiveness was evaluated using the area under the receiver operating characteristic curve (AUROC), focusing on its accuracy in predicting genomic HRD against a clinically recognized cutoff value.

Results: Our study demonstrated the predictability of genomic HRD status in endometrial, pancreatic, and lung cancers reaching cross-validated AUROCs of 0.79, 0.58, and 0.66, respectively. These predictions generalized well to an external cohort, with AUROCs of 0.93, 0.81, and 0.73. Moreover, a breast cancer-trained image-based HRD classifier yielded an AUROC of 0.78 in the internal validation cohort and was able to predict HRD in endometrial, prostate, and pancreatic cancer with AUROCs of 0.87, 0.84, and 0.67, indicating that a shared HRD-like phenotype occurs across these tumor entities.

Conclusions: This study establishes that HRD can be directly predicted from H&E slides using attMIL, demonstrating its applicability across nine different tumor types.

Keywords: Artificial intelligence; DNA repair mechanism; Deep learning; Homologous recombination deficiency; Mpathology; Pan-cancer study.

Fig. 1

Experimental design and study overview. A Overview of the different Homologous Recombination Deficiency (HRD) scores, their content, and assessment methods. B Workflow of our deep learning (DL) pipeline. A total of n = 9517 whole slide images (WSI) were processed and trained with an attention-based multiple instance learning (attMIL) approach. The statistical endpoint was the Area under the receiving operating curve (AUROC). C Study design for the three main experiments (internal fivefold cross-validation, tumor-wise external validation, and cross-cancer external validation) conducted and cohort overview for patients and tumor types included from The Cancer Genome Atlas (TCGA, n = 4113 patients) and Clinical Proteomic Tumor Analysis Consortium (CPTAC, n = 452 patients). Abbreviations: BRCA, breast invasive carcinoma; CRC, colorectal cancer; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PRAD, prostate adenocarcinoma; UCEC, uterine corpus endometrial carcinoma; HRR, Homologous recombination repair. This figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported licence

Fig. 2

Comparison of the area under the receiving operating curve (AUROC) for internal and tumor-wise external validation experiment models. Boxplot displaying the distribution of the AUROC and p-value (*p > 0.05; **p ≤ 0.05; ***p ≤ 0.01) for A internal fivefold cross-validation experiment of The Cancer Genome Atlas (TCGA) and tumor-wise external validation on the Clinical Proteomic Tumor Analysis Consortium (CPTAC); B AUROCs for the cross-cancer external validation experiment of the TCGA breast invasive carcinoma cohort (TCGA-BRCA) on the TCGA and CPTAC cohort. The horizontal line indicates the median, whereas each box represents the interquartile range (IQR) between the first and third quartiles. The whiskers extend from the box to the minimum and maximum values, considering 1.5 times the IQR. Abbreviations: BRCA, breast invasive carcinoma; CRC, colorectal cancer; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PRAD, prostate adenocarcinoma; UCEC, uterine corpus endometrial carcinoma

Fig. 3

Molecular characterization of The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort. A Distribution of breast cancer subtypes for the homologous recombination deficiency high (HRD-H) and low (HRD-L) ground truth subgroups. B Distribution of the breast cancer subtypes for the HRD-H and HRD-L deep learning (DL) predicted subgroups. C Alteration frequency for several genes of the HRD-H and HRD-L ground truth subgroups. D Alteration frequency for several genes of the HRD-H and HRD-L within cohort internal results prediction subgroups. E Grouped boxplots comparing the homologous recombination deficiency high (HRD-H) prediction scores with the mutational status (mutated = MUT, wildtype = WT) for the somatic and germline alterations of the BRCA1/2 genes. The central line represents the median value, while the box ranges between the first and third quartiles (IQR), and the whiskers extend to the lowest and highest values within 1.5 times the IQR. The y-axis represents the deep learning (DL) HRD-H prediction values. An independent t-test was performed to calculate the p-values (*p > 0.05; **p ≤ 0.05; ***p ≤ 0.01). This figure was created using https://www.cbioportal.org/ [59, 60]

Fig. 4

Visualization of predicted homologous recombination deficiency high (HRD-H) tumor samples. A Whole slide image (WSI) of an HRD-H predicted patient (ID: C3L-00358–21) from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) uterine corpus endometrial carcinoma (UCEC) cohort with magnification. B Attention heatmap for the same patient with magnification. C Classification Heatmap for the same patient with magnification. D Top predicted tiles for top three homologous recombination deficiency high (HRD-H) patients in The Cancer Genome Atlas (TCGA) breast invasive carcinoma (BRCA). E Top predicted tiles for three HRD-H patients in the CPTAC-UCEC cohort