The prevalence of homologous recombination deficiency (HRD) in various solid tumors and the role of HRD as a single biomarker to immune checkpoint inhibitors

Purpose Homologous recombination deficiency (HRD) is related to tumorigenesis. Currently, the possibility of HRD as a prognostic biomarker to immune checkpoint inhibitors is unknown. We aimed to investigate whether HRD has potential as a biomarker for immunotherapy. Methods The status of homologous recombination deficiency (HRD) was assessed with the next-generation sequencing (NGS) TruSight™ Oncology 500 assay in 501 patients with advanced solid tumor including gastrointestinal (GI), genitourinary (GU), or rare cancer. Results: among the 501 patients, HRD was observed as follows: 74.7% (347/501) patients; GU cancer (92.0%, 23 of 25), colorectal cancer (CRC) (86.1%, 130 of 151), hepatocellular carcinoma (HCC) (83.3%, 10 of 12), pancreatic cancer (PC) (76.2%, 32 of 42), biliary tract cancer (BTC) (75.0%, 36 of 48), sarcoma (65.0%, 39 of 60), melanoma (52.4%, 11 of 21), other GI cancers (50.0%, 11 of 22), and rare cancer (50.0%, 2 of 4). Sixty-five of the 501 patients had received immune checkpoint inhibitors (ICIs) during the course of the disease. Tumor types of 65 patients treated with ICIs are as follows: melanoma (95.2%, 20 of 21), HCC (33.3%, 4 of 12), rare cancer (25.0%, 1 of 4), GC (12.2%, 14 of 116), BTC (10.4%, 5 of 48), and sarcoma (5.0%, 3 of 60). The most frequently reported mutations were BRCA2 (n = 90), ARID1A (n = 77), ATM (n = 71), BARD1 (n = 67). Patients without HRD exhibited an objective response rate (ORR) of 33.3% (4 of 12), and patients with HRD exhibited an ORR of 34.0% (18 of 53). There was no significant difference in ORR between patients with and without HRD (P = 0.967). Progression-free survival (PFS) was 6.5 months (95% CI 0.000–16.175) in patients without HRD and 4.1 months (95% CI 2.062–6.138) in patients with HRD, revealing no statistical significance (P = 0.441). Conclusion Herein, we reported the status of HRD using a cancer-panel for various solid tumor patients in routine clinical practice and demonstrated that HRD as a single biomarker was not sufficient to predict efficacy of ICIs in solid tumor patients. Supplementary Information The online version contains supplementary material available at 10.1007/s00432-021-03781-6.


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Background After immune checkpoint inhibitors (ICIs) were introduced for treatment of solid tumors, they exhibited improved survival and treatment outcomes compared to traditional non-immune anti-cancer therapies, especially for patients with advanced melanoma, non-small-cell lung cancer (NSCLC), urothelial cancer (UC), renal cell carcinoma (RCC), or other cancer types (Borghaei et al. 2015;Hodi et al. 2010;Motzer et al. 2015Motzer et al. , 2018Nghiem et al. 2016;Rosenberg et al. 2016;Wolchok et al. 2013). However, only some patients achieved a response to ICIs. This indicates the need for further development of immunerelevant biomarkers to identify patients who might benefit from immunotherapy.
The DNA damage repair (DDR) system is essential to maintain the integrity of the genome in organisms. Genomic alteration due to failure to repair DDR causes tumor initiation. The homologous recombination (HR) pathway has a substantial influence on genomic integrity and germline mutations in this pathway and is related to tumorigenesis (Bartkova et al. 2005;Jeggo et al. 2016;Khanna 2015). Homologous recombination is one of the major repair mechanisms of DNA double-strand breaks. Homologous recombination deficiency (HRD) is a DNA repair deficiency related to tumorigenesis and causes increased sensitivity to platinum-based chemotherapy and PARP inhibitors (Watkins et al. 2014). The concept of therapy-directed HRD is approved in ovarian and breast cancers. The mutation in the HR pathway related to BRCA1/2 was used to predict better objective response rates to platinum-based chemotherapy in advanced triplenegative breast cancer (Tutt et al. 2018).
Recently, targeted cancer gene panel assay or NGS for HRD has been performed in clinical settings. These panels assess genomic profiles including Tumor Mutational Burden (TMB), Microsatellite Instability (MSI), and HRD. To date, the clinical significance of gene mutations related to HRD has not been studied well across various solid tumors. Herein, we analyzed the status of HRD using cancer panels for various solid tumor patients in routine clinical practice and determined the value of HRD as a biomarker of response to ICIs.

Patients
Patients with pathologic confirmation of advanced gastrointestinal, GU, or rare cancers at Samsung Medical Center between Oct 2019 and Mar 2020 (n = 501), were prospectively tested for molecular aberrations, including TMB, with the TruSight ™ Oncology 500 assay. All study participants provided written informed consent before study entry. The following clinicopathologic characteristics were collected for all patients: age, sex, primary tumor site, number of metastatic sites, site of metastasis, treatment, and survival. The study protocol was approved (#2020-11-151) by the Institutional Review Board of Samsung Medical Center (Seoul, Korea) and was conducted in accordance with the ethical principles of the Declaration of Helsinki and the Korea Good Clinical Practice guidelines. All patients provided written informed consent before enrollment. Written informed consent included disclosure of information, competency to make a decision, and voluntary nature of the decision for the purpose, benefit, and potential risk of this study.

Tumor samples
Samples for analysis were collected from 501 solid tumors and prepared as formalin-fixed paraffin-embedded (FFPE) material. The samples were gathered through biopsy at diagnosis, surgical specimen, or repeat biopsy at the time of disease progression; all were obtained before immunotherapy. The types of samples used in the analysis were as follows: biopsied samples (n = 320, 63.9%) and surgically resected samples (n = 181, 36.1%).

TruSight ™ oncology 500assay
Forty (40) ng of DNA was quantified with the Qubit dsDNA HS Assay (Thermo Fisher Scientific) on the Qubit 2.0 Fluorometer (Thermo Fisher Scientific) and then sheared using a Covaris E220 Focused-ultrasonicator (Woburn, MA, USA) and the 8 microTUBE-50 Strip AFA Fiber V2 following the manufacturer's instructions. Treatment time was optimized for FFPE material. The treatment settings were as follows: peak incident power (W): 75; duty factor: 15%; cycles per burst: 500; treatment time (s): 360; temperature (°C): 7; and water level: 6. For DNA library preparation and enrichment, the TruSight™ Oncology 500 Kit (Illumina) was used following the manufacturer's instructions. Post-enriched libraries were quantified, pooled, and sequenced on a NextSeq 500 (Illumina Inc., San Diego, CA, USA). The quality of the NextSeq 500 (Illumina) sequencing runs was assessed with the Illumina Sequencing Analysis Viewer (Illumina). Sequencing data were analyzed with the TruSight ™ Oncology 500 Local App Version 1.3.0.39 (Illumina), a comprehensive tumor profiling assay designed to identify known and emerging tumor biomarkers, including small variants, splice variants, and fusions. The reads were aligned to the reference genome (GRCh37/hg19) using Burrows − Wheeler Aligner-MEM (BWA-MEM) (Li 2013). Poorly mapped reads with a mapping quality (MAPQ) below 20 were removed using Samtools version 1.3.1 (Li et al. 2009). Somatic mutations including single-nucleotide variants (SNV) and small insertions and deletions (INDELs) were detected by the Pisces and Psara (Dunn et al. 2019). The rest of pipeline are as follows: CRAFT for copy number variation, TmbRaider for TMB, Hubble for MSI, STAR for RNA alignment, and Manta for fusion calling (Pestinger et al. 2020). Outputs of data, exported from The TSO 500 pipeline (Pestinger et al. 2020) were annotated with Ensembl Variant Effect Predictor (VEP) Annotation Engine, with information from the databases, such as dbSNP, gnomAD genome and exome, 1000 genomes, ClinVar, COSMIC, RefSeq, and Ensembl. The processed genomic alterations were categorized with four-tier system by American Society of Clinical Oncology and College of American Pathologists (Li et al. 2017), annotated with proper reference. The following criteria were used to filter our less significant variants and possible germline variants: (i) variants < 5% allele frequency and < 100 × read depth at the variant were excluded; (ii) variants previously reported to be benign or likely benign in the ClinVar archive (Landrum et al. 2016) were excluded; (iii) variant with a frequency greater than 1% in gnomAD (Karczewski et al. 2020) were excluded. Importantly, the TruSight ™ Oncology 500 measures homologous recombination deficiency (HRD). The HRDrelated genes were as follows: ARID1A, ATM, ATRX, BAP1, BARD1,BLM,BRCA1,BRCA2,BRIP1,CHEK1,CHEK2,FANCA,FANCC,FANCD2,FANCE,FANCF,FANCG,FANCL,MRE11A,NBN,PALB2,PTEN,RAD50,RAD51,and RAD51B. Homologous recombination deficiency was diagnosed if there was at least one HR-related gene mutation.

Statistical analyses and disease evaluation
All statistical analyses were conducted with SPSS statistics 27. Descriptive statistics are reported as proportion and   (Eisenhauer et al. 2009;Schwartz et al. 2016).
Objective response rate (ORR) was defined as the percentage of patients with complete response (CR) or partial response (PR). Progression-free survival (PFS) was defined as the interval between the initiation of the treatment and the time of progressive disease (PD). Logistic regression analysis was performed to analyze HRD genes that might be related to treatment response. A Cox regression model was used to analyze the associations of suspecting factors, including HRD and disease progression after ICIs treatment. The Mann-Whitney test was used to compare the difference between HRD and non-HRD. Kaplan-Meier estimates and log-rank tests were used in analysis of all time to event variables, and 95% confidence interval for the median time to event was computed.

Frequency of tumors with HRD according to type
Tumors with HRD were observed in 375 of 501 patients irrespective of type.  Figure 1 presents the distribution relationship with other biomarkers. All MSI were TMB-high and HR-deficient. However, some TMB-high have no HR deficiency. Figure 2A shows the percentage of confirmed HRD for each tumor type listed in order of high frequency rate. The tumor with the highest frequency of HRD was GU cancer with 92.0% and the lowest frequency was other GI tract cancer (AOV cancer, appendiceal cancer, cecal cancer, duodenal cancer, and GIST) and rare cancer at 50.0%. The distribution of HRD mutations for each cancer type is included in the supplement. (Supplement S1).

Frequency of HRD according to HR-related genes
We also analyzed the observed genetic variations by HRrelated genes. (Fig. 2B) The most frequently reported mutations were BRCA2 (n = 90), ARID1A (n = 77), ATM (n = 71), BARD1 (n = 67). On the other hand, FANCG was observed twice and BAP1 gene was reported only once. Even MRE11A gene was never observed in 501 patients' NGS results.
Detailed data on HRD status and progression for each cancer type are included in the supplement (Supplement S2).
Additionally, we conducted Cox proportional hazard analysis for PFS after ICIs (Table 3). TMB was the only meaningful prognostic factor (P = 0.019). Response after immunotherapy was analyzed logistic regression and only TMB was revealed to be statistically significant (P = 0.004). It has been previously reported in our study that TMB by NGS panel is a useful predictor of immunotherapy (Kim et al. 2021).

Discussion
In the present study, we evaluated the prevalence of HRD in 501 patients with various solid tumors and investigated the role of HRD as a single biomarker to predict response to ICIs. The overall prevalence of HRD we analyzed was 74.7% (347/501) and especially, GU cancer and CRC had the HRD of the high frequency. In 65 patients with ICIs, there were no significant differences for ORR and PFS between patients with and without HRD (P = 0.967 and P = 0.441, respectively). These findings suggested that HRD as a single biomarker was not sufficient to predict the efficacy of ICIs in solid tumor patients.
The overall frequency of HRD we analyzed was 74.7% (347/501). This finding was not consistent with other studies about HRD. A previous study reported that the prevalence of HR-DDR mutations was 17.4% in multiple tumor types (Heeke et al. 2018). This discordance might be caused by different studied genes including different NGS panels and different genes defining HRD. Detection of HRD by the NGS panel has limitations. There is no established definition to assess HRD. Therefore, there are many different results among published papers about the prevalence of HRD. Furthermore, this difference might be caused by discrepancy between measurement of HRD with whole exome sequencing and NGS panels.
There are a few limitations to this study. First, it was a retrospective study, and clinically heterogeneous populations were subject to potential biases. Second, only the Asian population was assessed in the study, so differences in genomic profiles and clinical features between Western and Eastern patients with solid tumors were not considered. Also, this study included a relatively small proportion of patients who had been treated with ICIs, making it difficult to draw definite conclusions regarding biomarkers.
To assess HRD, loss heterozygosity, number of telomeric allelic imbalance and large-scale state transitions are needed (Konstantinopoulos et al. 2015;Patel et al. 2018). However, these parameters are not available in TSO 500 but instead provide point mutations of HR-related genes. Detecting point mutations in HR genes using DNA sequencing panels to identify HR-deficient tumor is previously described (Pellegrino et al. 2020;Polak et al. 2017). In a study with renal cell carcinoma, mutation in HR-related gene associated with higher mutation burden in association with disease control (Labriola et al. 2020) and germline or somatic mutation of BRCA were associate with high mutational burden and showed different genetic character in breast cancer (Lal et al. 2019). The clinical significance of mutation in HR-related genes for application in immunotherapy still needs further investigation with larger cohort and sufficient follow-up period. In addition, future studies on the selection and cutoff value for HR-related gene numbers are also expected, as a biomarker development.
HRD might be a potential candidate predictor of response to ICIs, but the prevalence of HRD has not been investigated across tumor types. The present analysis produced useful information on the prevalence of HRD in various solid tumors under routine clinical practice and demonstrated that HRD as a single biomarker was not sufficient to predict the efficacy of ICIs in solid tumor patients.

Conclusion
Herein, we reported the status of HRD using a cancer panel for various solid tumor patients in routine clinical practice and demonstrated that HRD as a single biomarker was not sufficient to predict efficacy of ICIs in solid tumor patients.
Data availability All data that can prove the conclusion of this article are included in the article and the supplements.

Conflict of interest
The authors have no conflicts of interest to declare.
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