Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes as a predictive biomarker for immune checkpoint inhibitors in biliary tract cancer.

PURPOSE: Anti-PD-1/L1 has been demonstrated for its efficacy when combined with cytotoxic chemotherapy in randomized phase 3 trials for advanced biliary tract cancer (BTC). However, no biomarker predictive of benefit has been established for anti-PD-1/L1 in BTC. Here, we evaluated tumor-infiltrating lymphocytes (TILs) using artificial intelligence-powered immune phenotype (AI-IP) analysis in advanced BTC treated with anti-PD-1. PATIENTS AND METHODS: Pre-treatment H&E-stained whole-slide images from 339 advanced BTC patients who received anti-PD-1 as second-line treatment or beyond, were utilized for AI-IP analysis and correlative analysis between AI-IP and efficacy outcomes with anti-PD-1. Next, data and images of BTC cohort from The Cancer Genome Atlas (TCGA) were additionally analyzed to evaluate the transcriptomic and mutational characteristics of various AI-IPs in BTC. RESULTS: Overall, AI-IPs were classified as inflamed (high intratumoral TIL [iTIL]) in 40 patients (11.8%), immune-excluded (low iTIL and high stromal TIL) in 167 (49.3%), and immune-deserted (low TIL overall) in 132 (38.9%). The inflamed IP group showed a significantly higher overall response rate compared to the non-inflamed IP groups (27.5% vs. 7.7%, P<0.001). Median overall survival and progression-free survival were significantly longer in the inflamed IP group than in the non-inflamed IP group (OS: 12.6 vs. 5.1 months, P=0.002; PFS: 4.5 vs. 1.9 months, P<0.001). In the analysis using TCGA cohort, the inflamed IP showed increased cytolytic activity scores and an interferon-gamma signature compared to the non-inflamed IP. CONCLUSIONS: AI-powered IP based on spatial TIL analysis was effective in predicting the efficacy outcomes in patients with BTC treated with anti-PD-1.

An artificial intelligence-powered PD-L1 combined positive score (CPS) analyser in urothelial carcinoma alleviating interobserver and intersite variability.

AIMS: Immune checkpoint inhibitors targeting programmed death-ligand 1 (PD-L1) have shown promising clinical outcomes in urothelial carcinoma (UC). The combined positive score (CPS) quantifies PD-L1 22C3 expression in UC, but it can vary between pathologists due to the consideration of both immune and tumour cell positivity. METHODS AND RESULTS: An artificial intelligence (AI)-powered PD-L1 CPS analyser was developed using 1,275,907 cells and 6175.42 mm2 of tissue annotated by pathologists, extracted from 400 PD-L1 22C3-stained whole slide images of UC. We validated the AI model on 543 UC PD-L1 22C3 cases collected from three institutions. There were 446 cases (82.1%) where the CPS results (CPS ≥10 or <10) were in complete agreement between three pathologists, and 486 cases (89.5%) where the AI-powered CPS results matched the consensus of two or more pathologists. In the pathologist's assessment of the CPS, statistically significant differences were noted depending on the source hospital (P = 0.003). Three pathologists reevaluated discrepancy cases with AI-powered CPS results. After using the AI as a guide and revising, the complete agreement increased to 93.9%. The AI model contributed to improving the concordance between pathologists across various factors including hospital, specimen type, pathologic T stage, histologic subtypes, and dominant PD-L1-positive cell type. In the revised results, the evaluation discordance among slides from different hospitals was mitigated. CONCLUSION: This study suggests that AI models can help pathologists to reduce discrepancies between pathologists in quantifying immunohistochemistry including PD-L1 22C3 CPS, especially when evaluating data from different institutions, such as in a telepathology setting.

Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types.

BACKGROUND: The inflamed immune phenotype (IIP), defined by enrichment of tumor-infiltrating lymphocytes (TILs) within intratumoral areas, is a promising tumor-agnostic biomarker of response to immune checkpoint inhibitor (ICI) therapy. However, it is challenging to define the IIP in an objective and reproducible manner during manual histopathologic examination. Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types. METHODS: Lunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. We evaluated the correlation between the IIP and ICI treatment outcomes in terms of objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) in a cohort of 1,806 ICI-treated patients representing over 27 solid tumor types retrospectively collected from multiple institutions. RESULTS: We observed an overall IIP prevalence of 35.2% and significantly more favorable ORRs (26.3% vs 15.8%), PFS (median 5.3 vs 3.1 months, HR 0.68, 95% CI 0.61 to 0.76), and OS (median 25.3 vs 13.6 months, HR 0.66, 95% CI 0.57 to 0.75) after ICI therapy in IIP compared with non-IIP patients, respectively (p<0.001 for all comparisons). On subgroup analysis, the IIP was generally prognostic of favorable PFS across major patient subgroups, with the exception of the microsatellite unstable/mismatch repair deficient subgroup. CONCLUSION: The AI-based IIP may represent a practical, affordable, clinically actionable, and tumor-agnostic biomarker prognostic of ICI therapy response across diverse tumor types.

Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases.

BACKGROUND: Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) status. However, variability in interpretation among pathologists pose challenges to the accuracy of this classification. This study evaluates the role of artificial intelligence (AI) in enhancing the consistency of these evaluations. METHODS: AI-powered HER2 and ER/PR analyzers, consisting of cell and tissue models, were developed using 1,259 HER2, 744 ER, and 466 PR-stained immunohistochemistry (IHC) whole-slide images of breast cancer. External validation cohort comprising HER2, ER, and PR IHCs of 201 breast cancer cases were analyzed with these AI-powered analyzers. Three board-certified pathologists independently assessed these cases without AI annotation. Then, cases with differing interpretations between pathologists and the AI analyzer were revisited with AI assistance, focusing on evaluating the influence of AI assistance on the concordance among pathologists during the revised evaluation compared to the initial assessment. RESULTS: Reevaluation was required in 61 (30.3%), 42 (20.9%), and 80 (39.8%) of HER2, in 15 (7.5%), 17 (8.5%), and 11 (5.5%) of ER, and in 26 (12.9%), 24 (11.9%), and 28 (13.9%) of PR evaluations by the pathologists, respectively. Compared to initial interpretations, the assistance of AI led to a notable increase in the agreement among three pathologists on the status of HER2 (from 49.3 to 74.1%, p < 0.001), ER (from 93.0 to 96.5%, p = 0.096), and PR (from 84.6 to 91.5%, p = 0.006). This improvement was especially evident in cases of HER2 2+ and 1+, where the concordance significantly increased from 46.2 to 68.4% and from 26.5 to 70.7%, respectively. Consequently, a refinement in the classification of breast cancer molecular subtypes (from 58.2 to 78.6%, p < 0.001) was achieved with AI assistance. CONCLUSIONS: This study underscores the significant role of AI analyzers in improving pathologists' concordance in the classification of breast cancer molecular subtypes.

Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes as a predictive biomarker for axitinib in adenoid cystic carcinoma.

BACKGROUND: This study analyzed the predictive value of artificial intelligence (AI)-powered tumor-infiltrating lymphocyte (TIL) analysis in recurrent or metastatic (R/M) adenoid cystic carcinoma (ACC) treated with axitinib. METHODS: Patients from a multicenter, prospective phase II trial evaluating axitinib efficacy in R/M ACC were included in this study. H&E whole-side images of archival tumor tissues were analyzed by Lunit SCOPE IO, an AI-powered spatial TIL analyzer. RESULTS: Twenty-seven patients were included in the analysis. The best response was stable disease, and the median progression-free survival (PFS) was 11.1 months (95% CI, 9.2-13.7 months). Median TIL densities in the cancer and surrounding stroma were 25.8/mm2 (IQR, 8.3-73.0) and 180.4/mm2 (IQR, 69.6-342.8), respectively. Patients with stromal TIL density >342.5/mm2 exhibited longer PFS (p = 0.012). CONCLUSIONS: Cancer and stromal area TIL infiltration were generally low in R/M ACC. Higher stromal TIL infiltration was associated with a longer PFS with axitinib treatment.

Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer.

Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.

Papillary thyroid cancer immune phenotypes via tumor-infiltrating lymphocyte spatial analysis.

Standard-of-care treatment options provide an excellent prognosis for papillary thyroid cancers (PTCs); however, approximately 10% of cases are advanced PTCs, resulting in less than 50% 5-year survival rates. Understanding the tumor microenvironment is essential for understanding cancer progression and investigating potential biomarkers for treatment, such as immunotherapy. Our study focused on tumor-infiltrating lymphocytes (TILs), which are the main effectors of antitumor immunity and related to the mechanism of immunotherapy. Using an artificial intelligence model, we analyzed the density of intratumoral and peritumoral TILs in the pathologic slides of The Cancer Genome Atlas PTC cohort. Tumors were classified into three immune phenotypes (IPs) based on the spatial distribution of TILs: immune-desert (48%), immune-excluded (34%), and inflamed (18%). Immune-desert IP was mostly characterized by RAS mutations, high thyroid differentiation score, and low antitumor immune response. Immune-excluded IP predominantly consisted of BRAF V600E-mutated tumors and had a higher rate of lymph node metastasis. Inflamed IP was characterized by a high antitumor immune response, as demonstrated by a high cytolytic score, immune-related cell infiltrations, expression of immunomodulatory molecules (including immunotherapy target molecules), and enrichment of immune-related pathways. This study is the first to investigate IP classification using TILs in PTC through a tissue-based approach. Each IP had unique immune and genomic profiles. Further studies are warranted to assess the predictive value of IP classification in advanced PTC patients treated with immunotherapy.

Artificial Intelligence-Powered Whole-Slide Image Analyzer Reveals a Distinctive Distribution of Tumor-Infiltrating Lymphocytes in Neuroendocrine Neoplasms.

Despite the importance of tumor-infiltrating lymphocytes (TIL) and PD-L1 expression to the immune checkpoint inhibitor (ICI) response, a comprehensive assessment of these biomarkers has not yet been conducted in neuroendocrine neoplasm (NEN). We collected 218 NENs from multiple organs, including 190 low/intermediate-grade NENs and 28 high-grade NENs. TIL distribution was derived from Lunit SCOPE IO, an artificial intelligence (AI)-powered hematoxylin and eosin (H&E) analyzer, as developed from 17,849 whole slide images. The proportion of intra-tumoral TIL-high cases was significantly higher in high-grade NEN (75.0% vs. 46.3%, p = 0.008). The proportion of PD-L1 combined positive score (CPS) ≥ 1 case was higher in high-grade NEN (85.7% vs. 33.2%, p < 0.001). The PD-L1 CPS ≥ 1 group showed higher intra-tumoral, stromal, and combined TIL densities, compared to the CPS < 1 group (7.13 vs. 2.95, p < 0.001; 200.9 vs. 120.5, p < 0.001; 86.7 vs. 56.1, p = 0.004). A significant correlation was observed between TIL density and PD-L1 CPS (r = 0.37, p < 0.001 for intra-tumoral TIL; r = 0.24, p = 0.002 for stromal TIL and combined TIL). AI-powered TIL analysis reveals that intra-tumoral TIL density is significantly higher in high-grade NEN, and PD-L1 CPS has a positive correlation with TIL densities, thus showing its value as predictive biomarkers for ICI response in NEN.

Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework.

Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.

Artificial Intelligence-Powered Hematoxylin and Eosin Analyzer Reveals Distinct Immunologic and Mutational Profiles among Immune Phenotypes in Non-Small-Cell Lung Cancer.

The tumor microenvironment can be classified into three immune phenotypes: inflamed, immune excluded, and immune-desert. Immunotherapy efficacy has been shown to vary by phenotype; yet, the mechanisms are poorly understood and demand further investigation. This study unveils the mechanisms using an artificial intelligence-powered software called Lunit SCOPE. Artificial intelligence was used to classify 965 samples of non-small-cell lung carcinoma from The Cancer Genome Atlas into the three immune phenotypes. The immune and mutational profiles that shape each phenotype using xCell, gene set enrichment analysis with RNA-sequencing data, and cBioportal were described. In the inflamed subtype, which showed higher cytolytic score, the enriched pathways were generally associated with immune response and immune-related cell types were highly expressed. In the immune excluded subtype, enriched glycolysis, fatty acid, and cholesterol metabolism pathways were observed. The KRAS mutation, BRAF mutation, and MET splicing variant were mostly observed in the inflamed subtype. The two prominent mutations found in the immune excluded subtype were EGFR and PIK3CA mutations. This study is the first to report the distinct immunologic and mutational landscapes of immune phenotypes, and demonstrates the biological relevance of the classification. In light of these findings, the study offers insights into potential treatment options tailored to each immune phenotype.

Comparative efficacy of subcutaneous (CT-P13) and intravenous infliximab in adult patients with rheumatoid arthritis: a network meta-regression of individual patient data from two randomised trials.

BACKGROUND: A subcutaneous (SC) formulation of infliximab biosimilar CT-P13 is approved in Europe for the treatment of adult patients with rheumatoid arthritis (RA). It may offer improved efficacy versus intravenous (IV) infliximab formulations. METHODS: A network meta-regression was conducted using individual patient data from two randomised trials in patients with RA, which compared CT-P13 SC with CT-P13 IV, and CT-P13 IV with reference infliximab IV. In this analysis, CT-P13 SC was compared with CT-P13 IV, reference infliximab IV and pooled data for both reference infliximab IV and CT-P13 IV. Outcomes included changes from baseline in 28-joint Disease Activity Score based on C-reactive protein (DAS28-CRP), Simplified Disease Activity Index (SDAI) and Clinical Disease Activity Index (CDAI), and rates of remission, low disease activity or clinically meaningful improvement in functional disability per Health Assessment Questionnaire-Disability Index (HAQ-DI). RESULTS: The two studies enrolled 949 patients with RA; pooled data for 840 and 751 patients were evaluable at weeks 30 and 54, respectively. For the CT-P13 SC versus pooled IV treatment arm comparison, differences in changes from baseline in DAS28-CRP (- 0.578; 95% confidence interval [CI] - 0.831, - 0.325; p < 0.0001), CDAI (- 3.502; 95% CI - 5.715, - 1.289; p = 0.002) and SDAI (- 4.031; 95% CI - 6.385, - 1.677; p = 0.0008) scores at 30 weeks were statistically significant in favour of CT-P13 SC. From weeks 30 to 54, the magnitude of the differences increased and remained statistically significant in favour of CT-P13 SC. Similar results were observed for the comparison of CT-P13 SC with CT-P13 IV and with reference infliximab IV. Statistically significant differences at week 30 favoured CT-P13 SC over the pooled IV treatment arms for the proportions of patients achieving EULAR-CRP good response, American College of Rheumatology (ACR) 50 and ACR70 responses, DAS28-CRP-defined remission, low disease activity (DAS28-CRP, CDAI and SDAI criteria) and clinically meaningful HAQ-DI improvement. CONCLUSIONS: CT-P13 SC was associated with greater improvements in DAS28-CRP, CDAI and SDAI scores and higher rates of clinical response, low disease activity and clinically meaningful improvement in functional disability, compared with CT-P13 IV and reference infliximab IV. TRIAL REGISTRATION: EudraCT, 2016-002125-11 , registered 1 July 2016; EudraCT 2010-018646-31 , registered 23 June 2010.

Efficacy and safety of subcutaneous infliximab versus adalimumab, etanercept and intravenous infliximab in patients with rheumatoid arthritis: a systematic literature review and meta-analysis.

OBJECTIVES: There are few comparative data for tumor necrosis factor inhibitors in patients with rheumatoid arthritis (RA). METHODS: Historical data for reference product/biosimilar intravenous infliximab, or adalimumab and etanercept, were pooled and compared with phase 3 study results for a subcutaneous (SC) formulation of the infliximab biosimilar CT-P13, in a systematic review and meta-analysis (PROSPERO: CRD42019149621). RESULTS: The authors identified 13 eligible controlled trials that randomized over 5400 participants to prespecified treatments of interest. Comparison with pooled historical data suggested a numerical advantage for CT-P13 SC over intravenous infliximab for almost every prespecified efficacy outcome evaluated, including Disease Activity Score in 28 joints (C-reactive protein/erythrocyte sedimentation rate), Clinical/Simplified Disease Activity Index scores, American College of Rheumatology responses, and multiple measures of disease remission and low disease activity; for the majority of outcomes, there was no overlap in 95% confidence intervals between groups. A numerical advantage for CT-P13 SC was also observed for safety outcomes (adverse events, infections, and discontinuations). Similar, but less marked, trends were observed for comparison with historical efficacy and safety data for adalimumab/etanercept. CONCLUSION: CT-P13 SC offers an improved or similar benefit-to-harm ratio compared with infliximab (intravenous) and adalimumab/etanercept, for the treatment of moderate-to-severe RA.

The Effect of Polydeoxyribonucleotide on Chronic Non-healing Wound of an Amputee: A Case Report.

Polydeoxyribonucleotide (PDRN) is safe and effective in wound healing, cellular growth, synthesis of extracellular matrix protein, and inflammation reduction via activation of adenosine A2 receptors. We report a 28-year-old male patient treated with PDRN injections for chronic non-healing wound refractory to negative pressure wound therapy, skin graft, or growth factors. Three injections of PDRN were administered at the wound site into the anterior and medial sides of the left stump on the 1st, 4th, and 9th days of hospitalization. The PDRN ameliorated wound healing by enhancing cell growth, tissue repair, and angiogenesis. PDRN application represents a potential treatment for non-healing wounds obviating the need for additional therapies, and hospitalization, as well as improve patient's activities of daily living.

Utility of targeted deep sequencing for detecting circulating tumor DNA in pancreatic cancer patients.

Targeted deep sequencing across broad genomic regions has been used to detect circulating tumor DNA (ctDNA) in pancreatic ductal adenocarcinoma (PDAC) patients. However, since most PDACs harbor a mutation in KRAS, sequencing of broad regions needs to be systemically compared to analyzing only KRAS mutations for PDAC. Using capture-based targeted deep sequencing, we detected somatic tumor mutations in 17 fine needle aspiration biopsy and 69 longitudinal cell-free DNA (cfDNA) samples from 17 PDAC patients. KRAS mutations were detected in 10 out of 17 pretreatment patient plasma samples. Next, interrogation of genetic alterations in matched primary tumor samples detected ctDNA in 12 of 17 pretreatment plasma samples and cfDNA sequencing across the 83 target genes identified ctDNA in 15 of 17 cases (88.2% sensitivity). This improved sensitivity of ctDNA detection resulted in enhanced tumor burden monitoring when we analyzed longitudinal plasma samples. We found that cfDNA sequencing detected the lowest mutant allelic fractions and number of variants when complete response or partial response to chemotherapy was achieved. We demonstrated that ctDNA levels measured by targeted deep sequencing sensitively indicate the presence of cancer and correlate well with clinical responses to therapy and disease progression in PDAC patients.

Lymphedema Associated With Primary Amyloidosis: A Case Study.

We reported on a 60-year-old man presenting lymphedema of both lower extremities and scrotum for 3 years with unknown cause. We took a computed tomography scan of the lower extremities as a follow-up. There were diffuse subcutaneous edema in both lower extremities and multiple enlarged lymph nodes along the para-aortic and bilateral inguinal areas. For further evaluation, biopsy of an enlarged inguinal lymph node was taken, yielding a diagnosis of primary amyloidosis. A treatment of chemotherapy for amyloidosis was recommended for him. To our knowledge, this is the first report of lymphedema presenting with primary amyloidosis in Asia. This case suggests that primary amyloidosis could be one of the differential diagnoses in patients with lymphedema in the lower extremities.

Circulating tumor DNA shows variable clonal response of breast cancer during neoadjuvant chemotherapy.

Circulating tumor DNA (ctDNA) correlates with tumor burden and provides early detection of treatment response and tumor genetic alterations in breast cancer (BC). In this study, we aimed to identify genetic alterations during the process of tumor clonal evolution and examine if ctDNA level well indicated clinical response to neoadjuvant chemotherapy (NAC) and BC recurrence. We performed targeted ultra-deep sequencing of plasma DNAs, matched germline DNAs and tumor DNAs from locally advanced BC patients. Serial plasma DNAs were collected at diagnosis, after the 1st cycle of NAC and after curative surgery. For the target enrichment, we designed RNA baits covering a total of ∼202kb regions of the human genome including a total of 82 cancer-related genes. For ctDNA, 15 serial samples were collected and 87% of plasma SNVs were detected in 13 BC samples that had somatic alterations in tumor tissues. The TP53 mutation was most commonly detected in primary tumor tissues and plasma followed by BRCA1 and BRCA2. At BC diagnosis, the amount of plasma SNVs did not correlate with clinical stage at diagnosis. With respect to the therapeutic effects of NAC, we found two samples in which ctDNA disappeared after the 1st NAC cycle achieved a pathologic complete response (pCR). In addition, the amount of ctDNA correlated with residual cancer volume detected by breast MRI. This targeted ultra-deep sequencing for ctDNA analysis would be useful for monitoring tumor burden and drug resistance. Most of all, we suggest that ctDNA could be the earliest predictor of NAC response.

Characterization of background noise in capture-based targeted sequencing data.

BACKGROUND: Targeted deep sequencing is increasingly used to detect low-allelic fraction variants; it is therefore essential that errors that constitute baseline noise and impose a practical limit on detection are characterized. In the present study, we systematically evaluate the extent to which errors are incurred during specific steps of the capture-based targeted sequencing process. RESULTS: We removed most sequencing artifacts by filtering out low-quality bases and then analyze the remaining background noise. By recognizing that plasma DNA is naturally fragmented to be of a size comparable to that of mono-nucleosomal DNA, we were able to identify and characterize errors that are specifically associated with acoustic shearing. Two-thirds of C:G > A:T errors and one quarter of C:G > G:C errors were attributed to the oxidation of guanine during acoustic shearing, and this was further validated by comparative experiments conducted under different shearing conditions. The acoustic shearing step also causes A > G and A > T substitutions localized to the end bases of sheared DNA fragments, indicating a probable association of these errors with DNA breakage. Finally, the hybrid selection step contributes to one-third of the remaining C:G > A:T and one-fifth of the C > T errors. CONCLUSIONS: The results of this study provide a comprehensive summary of various errors incurred during targeted deep sequencing, and their underlying causes. This information will be invaluable to drive technical improvements in this sequencing method, and may increase the future usage of targeted deep sequencing methods for low-allelic fraction variant detection.

Analyzing Somatic Genome Rearrangements in Human Cancers by Using Whole-Exome Sequencing.

Although exome sequencing data are generated primarily to detect single-nucleotide variants and indels, they can also be used to identify a subset of genomic rearrangements whose breakpoints are located in or near exons. Using >4,600 tumor and normal pairs across 15 cancer types, we identified over 9,000 high confidence somatic rearrangements, including a large number of gene fusions. We find that the 5' fusion partners of functional fusions are often housekeeping genes, whereas the 3' fusion partners are enriched in tyrosine kinases. We establish the oncogenic potential of ROR1-DNAJC6 and CEP85L-ROS1 fusions by showing that they can promote cell proliferation in vitro and tumor formation in vivo. Furthermore, we found that ∼4% of the samples have massively rearranged chromosomes, many of which are associated with upregulation of oncogenes such as ERBB2 and TERT. Although the sensitivity of detecting structural alterations from exomes is considerably lower than that from whole genomes, this approach will be fruitful for the multitude of exomes that have been and will be generated, both in cancer and in other diseases.

Standard immunohistochemistry efficiently screens for anaplastic lymphoma kinase rearrangements in differentiated thyroid cancer.

The anaplastic lymphoma kinase (ALK) gene is frequently rearranged in various types of cancer and is highly responsive to targeted therapeutics. We developed a system to detect rearrangement of ALK in a large group of Korean thyroid cancer patients. We screened 474 malignant or benign thyroid tumor cases to identify ALK fusions. Expression and translocation of the ALK gene were analyzed by immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and digital multiplexed gene expression (DMGE) analysis in formalin-fixed paraffin-embedded tissues. Four cases of rearrangement of ALK were detected by IHC, and these cases were validated with FISH on 189 samples. On the other hand, DMGE analysis using Nanostring detected three out of four IHC-positive cases. Two rearrangements of ALK were striatin (STRN)-ALK fusions, which were identified by 5' RACE analysis. Rearrangements of ALK were found exclusively in v-raf murine sarcoma viral oncogene homolog B (BRAF) WT papillary carcinomas. Given the wide availability and accuracy of IHC for detecting ectopic expression of ALK in the thyroid, we suggest that IHC-based screening can be a practical method for identifying patients with ALK rearrangements in differentiated thyroid cancer.