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Genomic Profiling of Combined Hepatocellular Cholangiocarcinoma Reveals Genomics Similar to Either Hepatocellular Carcinoma or Cholangiocarcinoma

PURPOSE Combined hepatocellular cholangiocarcinoma (cHCC-CCA) is a rare, aggressive primary liver carcinoma, with morphologic features of both hepatocellular carcinomas (HCC) and liver cholangiocarcinomas (CCA). METHODS The genomic profiles of 4,975 CCA, 1,470 HCC, and 73 cHCC-CCA cases arising from comprehensive genomic profiling in the course of clinical care were reviewed for genomic alterations (GA), tumor mutational burden, microsatellite instability status, genomic loss of heterozygosity, chromosomal aneuploidy, genomic ancestry, and hepatitis B virus status. RESULTS In cHCC-CCA, GA were most common in TP53 (65.8%), TERT (49.3%), and PTEN (9.6%), and 24.6% cHCC-CCA harbored potentially targetable GA. Other GA were predominantly associated with either HCC or CCA, including, but not limited to, TERT, FGFR2, IDH1, and presence of hepatitis B virus. On the basis of these features, a machine learning (ML) model was trained to classify a cHCC-CCA case as CCA-like or HCC-like. Of cHCC-CCA cases, 16% (12/73) were ML-classified as CCA-like and 58% (42/73) cHCC-CCA were ML-classified as HCC-like. The ML model classified more than 70% of cHCC-CCA as CCA-like or HCC-like on the basis of genomic profiles, without additional clinico-pathologic input. CONCLUSION These findings demonstrate the use of ML for classification as based on a targeted exome panel used during routine clinical care. Classification of cHCC-CCA by genomic features alone creates insights into the biology of the disease and warrants further investigation for relevance to clinical care.


INTRODUCTION
Combined hepatocellular carcinoma cholangiocarcinoma 1 (cHCC-CCA) is a rare primary liver malignancy with morphologic features of both hepatocellular carcinomas (HCC) and liver cholangiocarcinomas (CCA). Estimates of the incidence of cHCC-CCA vary broadly from 0.4% to 5%, [2][3][4] with reference to being a fraction of the incidence of primary liver tumors. For disseminated or recurrent cHCC-CCA, no consensus guidelines exist because of the rarity of this disease, which is prohibitive for conducting diseasespecific clinical trials. By contrast, there are both numerous trials and consensus guidelines for the management of patients with HCC and CCA, and more recently the former often includes additional genomic inclusion criteria, for example, ongoing trials of FGFR2 inhibitors in FGFR2 fusion-positive CCA. 5 Histologically, cHCC-CCA can be further subdivided into separate, combined, and mixed subtypes on the basis of morphology; however, these classifications have no impact on clinical care. 6 Numerous studies have defined the characteristic genomic landscapes of HCC and CCA. [7][8][9][10] For cHCC-CCA, genomic studies are limited to small cohorts and have typically used whole-genome sequencing, wholeexome sequencing, and transcriptome sequencing, all retrospectively. [11][12][13] The recent seminal study of Xue et al 13 demonstrates the retrospective use of wholegenome sequencing and RNA sequencing on lasercapture microdissected samples, with additional specialized specimen handling for each cHCC-CCA subtype and additional immunohistochemistry studies. Importantly, this study suggests that the combined subtype of cHCC-CCA resembles CCA and the mixed subtype resembles HCC.
In this work, we used comprehensive genomic profiling (CGP) of formalin-fixed paraffin-embedded specimens of a large series of HCC, CCA, and cHCC-CCA cases, all sequenced as part of routine clinical care. We found genomic alterations (GA) enriched in HCC and CCA. On that basis, we built a machine learning (ML) model to classify cHCC-CCA as HCClike or CCA-like by integrating genomic-derived data including alterations, aneuploidy, ancestry, complex biomarker signatures, and hepatitis B virus (HBV) status. We demonstrate that a genomic classification of cHCC-CCA in the course of clinical care is possible and may drive the utility of targeted therapy in this challenging disease.

Comprehensive Genomic Profiling
This study was conducted in accordance with the Western Institutional Review Board-approved protocol no. 20152817. Clinical cHCC-CCA (N = 73), CCA (N = 4,975), and HCC (N = 1,470) cases, as diagnosed by the treating physician (and confirmed on hematoxylin and eosin-stained slides), underwent CGP in a Clinical Laboratory Improvement Amendments-certified, NY State-approved, College of American Pathologists-accredited laboratory (Foundation Medicine Inc, Cambridge, MA). 14 Manual microdissection was performed if warranted on pathologist visual inspection.
CGP on 0.8-1.1 Mb of the coding genome was performed on hybridization-captured, adapter-ligation-based libraries, to identify GA (base substitutions, small insertions or deletions, copy-number alterations, and rearrangements) in exons and select introns in up to 404 genes, tumor mutational burden (TMB), microsatellite instability status (MSI), genomic loss of heterozygosity (gLOH), chromosomal aneuploidy, genomic ancestry, and HBV status.
TMB was calculated as the number of nondriver somatic coding mutations per megabase of genome sequenced. 15 TMB high was defined as 20 mutations/Mb (mut/Mb) or higher. MSI status was determined by analyzing 114 intronic homopolymer repeat loci for length variability and MSI high was defined as described previously. 16 gLOH high (gLOH-H) was classified as 16% gLOH or higher, as was used in the ARIEL3 poly (ADP-ribose) polymerase inhibitor trial in ovarian cancer. 17,18 Genomic ancestry of patients was determined using a principal component analysis of genomic single-nucleotide polymorphisms trained on data from the 1,000 Genomes Project, and each patient was classified as belonging to one of the following super populations: AFR (African), AMR (Ad Mixed American), EAS (East Asian), EUR (European), and SAS (South Asian). 19,20 All GA prevalence reported in this study only include alterations described as functional or pathogenic in literature and seen in the Catalogue of Somatic Mutations in Cancer 21 repository or had a likely functional status (frameshift or truncation events in tumor suppressor genes). Variants of unknown significance were not studied.

Hepatitis B Virus Detection
Presence of HBV was determined by the identification of DNA sequences consistent with genomic HBV DNA. Sequencing reads left unmapped to the human reference genome (hg19) were de novo assembled by Velvet, 22,23 and the assembled contigs were competitively aligned by BLASTn 24 to the National Center for Biotechnology Information database of more than 3 million known viral nucleotide sequences. A positive viral status was determined by contigs at least 80 nucleotides in length and with at least 97% identity to the BLAST sequence. 25

Statistical Analysis
All statistical analyses were performed using R software (R Foundation for Statistical Computing, Vienna, Austria,

CONTEXT Key Objective
Combined hepatocellular cholangiocarcinoma (cHCC-CCA) is a rare cancer with morphologic features of both liver cholangiocarcinoma (CCA) and hepatocellular carcinoma (HCC). The rarity of cHCC-CCA poses challenges for evidence-guided management, and the origin of this disease remains enigmatic.

Knowledge Generated
This study analyzes 73 patients with cHCC-CCA who obtained comprehensive genomic profiling as part of routine clinical care and as compared to the comprehensive genomic profiling of 4,975 patients with CCA and patients with 1,470 HCC. We classify cHCC-CCA as HCC-like or CCA-like as based solely on the genomic profiles resulting from a targeted exome next generation sequencing assay used in the course of clinical care. Furthermore, we developed an artificial intelligence-driven machine learning classifier that leverages the well-known mutually exclusive genomics of CCA and HCC and demonstrate that genomic features alone can classify more than 70% of our cHCC-CCA cohort. v3.6.0). Proportions of categorical variables were compared using the chi-square test or Fisher's exact test; chi-square test was used for contingency tables larger than 2 × 2.
The Wilcoxon-rank sum test and the Kruskal-Wallis test were used to test for differences between continuous variables. All P values are two-sided, and multiple hypothesis testing correction was performed using the Benjamini-Hochberg procedure to calculate the false discovery rate.

Machine Learning
A random forest 26 -based ML model was trained on a highquality subset of CCA (n = 1,916) and HCC (n = 755), using genomic features (such as alterations, aneuploidy, signatures, ancestry, and HBV status) and clinicopathologic features (such as age at the time of CGP, sex of the patient, tissue biopsy site, and local or metastatic status of the tumor [Data Supplement]). Model performance was evaluated by 10-fold cross-validation of the training cohort and on an independent test cohort of 479 CCA and 189 HCC (Data Supplement). This model was then applied on each cHCC-CCA case to classify it as CCA-like, HCC-like, or ambiguous.

Patient and Specimen Characteristics
Liver biopsies were assayed for the majority of CCA, cHCC-CCA, and HCC cases. Patients with HCC were predominantly male, as described previously, 27 and were enriched for younger patients and the African and East Asian genomic ancestry, 28 whereas patients with CCA had a comparable sex prevalence, and were enriched for older patients and European genomic ancestry ( Table 1). The cHCC-CCA cohort (N = 73) was 71.2% male and 60.3% of European ancestry, with a median age of 62 years (range: 22 years to 89+ years; Table 1).
Within the cHCC-CCA cohort, the prevalence of CCA-and HCC-enriched genes described above trended toward mutual exclusivity, possibly defining independent genomic subpopulations ( Fig 2B). As such, the eight cases of cHCC-CCA positive for HBV were wild-type for genes often altered in CCA: ARID1A, BAP1, CDKN2B, FGFR2, IDH1, KRAS, and PBRM1.

Genomic Signatures
Biomarker signatures associated with response to immunotherapy like TMB high 29 and MSI high 30 were low in prevalence across the three diseases (Data Supplement). The median TMB was comparable across all three diseases: CCA (2.5 mut/Mb), HCC (3.5 mut/Mb), and cHCC-CCA (2.6 mut/Mb, Data Supplement). Alterations in the mismatch repair pathway were also seen in similar frequencies across all three diseases (data not shown).

Genomic Aneuploidy
Chromosome arm-level aneuploidy was highly prevalent and differentially enriched across the three forms of primary liver carcinomas. When CCA was compared with HCC, loss of 3p, 9p, 9q, and 6q and gain of 6p and 5q were significantly enriched in CCA and HCC, respectively (Fig 3A, Data  Supplement). Within the cHCC-CCA population, the most frequent events were 8q gain (46.7%), 1q gain (40.0%), 8p loss (33.3%), and 17p loss (26.7%). In total, we identified 39 unique chromosome arm-level gains and 33 unique chromosome arm-level losses across the 60 cases ( Fig 3B).

Machine Learning
The marked differences in the genomic features between CCA and HCC cases created an opportunity to build an ML model that could characterize a primary liver carcinoma as CCA-like or HCC-like.
We trained and tested a random forest-based classifier on a high-quality cohort of cases (training cohort: CCA n = 1,916 and HCC n = 755; independent test cohort: CCA n = 497 and HCC n = 189; Data Supplement). When the model was trained on genomic features alone, the model's sensitivity and specificity were 85.9% and 93.4%, respectively, and when the model was trained on genomic and clinicopathologic features, the model's sensitivity and specificity were 87.6% and 94.5%, respectively, based on 10-fold cross-validation of the training data (Data Supplement). Both the models performed similarly on the test cohort (Data Supplement), with a classification accuracy of 91% (95% CI, 88.8 to 93.2). When probed further, clinicopathologic features such as sex of the patient, biopsy site of the patient's tumor specimen, and age of the patient at the time of CGP testing were all found to be significantly associated with the presence or absence of genomic features including but not limited to TERT, CTNNB1, IDH1, and FGFR2 (Data Supplement), across the high-quality cohort of HCC and CCA. Subsequently, we chose the genomics-driven model for all further analyses. This classifier was then applied on each of the 73 cHCC-CCA cases, to place each individual case within the CCA-HCC spectrum (Fig 4A). A prediction call of CCA-like or HCC-like was made for 74% (54/73) of the cHCC-CCA cases. 0.61 was the probability cutoff used to separate HCC-like or CCAlike cHCC-CCA from ambiguous cHCC-CCA cases (Data Supplement); this value was derived from the probability threshold that maximized the Matthew's correlation coefficient 31 in the HCC-CCA training cohort. In total, 16.4% (12/73) of the cHCC-CCA cases were classified as CCA-like, 57.5% (42/73) as HCC-like, and the remaining 26.3% (19/ 73) of the cHCC-CCA cases were classified as ambiguous.

DISCUSSION
The two most frequent forms of primary liver carcinomas (HCC and CCA) are ontologically, morphologically, and clinically distinct. However, as evoked by its name, cHCC-CCA is a poorly understood, aggressive rare primary liver cancer that exhibits morphologic characteristics of both HCC and CCA. 32 As such, cHCC-CCA is an extremely challenging disease regarding both diagnosis and management.
Within individual cases, GA, signatures, and aneuploidy were strongly associated with either CCA or HCC, typically mutually exclusive for those associated with the other disease, suggesting that individual cases could be labeled as resembling either CCA or HCC. This led us to build a genomics-only-driven ML model to classify a cHCC-CCA case as CCA-like or HCC-like, which could then accurately classify more than 70% of the cases. Within our study cohort, the clinical characteristics examined were strongly associated with genomic features at a population level and as such did not add independent resolution to further strengthen classification in differentiating HCC from CCA. microdissection of areas with differing morphology, and a team of specialized pathologists for subtype classification were also used, and all this was performed on banked specimens dating from 2007 to 2018. By contrast, for the current study, the only assay used was a clinically validated targeted exome sequencing assay, at the request of a physician currently treating a patient with cHCC-CCA. If the results of this study are to become clinically impactful, then clincally validated targeted exome based sequencing assays would prove to be vital for future cHCC-CCA patients.
Furthermore, in this study, no histologic subclassification into the separate, combined, and mixed subtypes was performed, which is in contrast to Xue et al. However, the resolving power of the ML tool separated cHCC-CCA cases into HCC-like or CCA-like solely on the basis of genomic features, which indicates that subtype classification is not needed for classification.