Loss of IFN-γ Pathway Genes in Tumor Cells as a Mechanism of Resistance to Anti-CTLA-4 Therapy.

Antibody blockade of the inhibitory CTLA-4 pathway has led to clinical benefit in a subset of patients with metastatic melanoma. Anti-CTLA-4 enhances T cell responses, including production of IFN-γ, which is a critical cytokine for host immune responses. However, the role of IFN-γ signaling in tumor cells in the setting of anti-CTLA-4 therapy remains unknown. Here, we demonstrate that patients identified as non-responders to anti-CTLA-4 (ipilimumab) have tumors with genomic defects in IFN-γ pathway genes. Furthermore, mice bearing melanoma tumors with knockdown of IFN-γ receptor 1 (IFNGR1) have impaired tumor rejection upon anti-CTLA-4 therapy. These data highlight that loss of the IFN-γ signaling pathway is associated with primary resistance to anti-CTLA-4 therapy. Our findings demonstrate the importance of tumor genomic data, especially IFN-γ related genes, as prognostic information for patients selected to receive treatment with immune checkpoint therapy.

CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data.

Single-cell DNA sequencing (scDNA-seq) has been an effective means to unscramble intra-tumor heterogeneity, while joint inference of tumor clones and their respective copy number profiles remains a challenging task due to the noisy nature of scDNA-seq data. We introduce a new bioinformatics method called CoT for deciphering clonal copy number substructure. The backbone of CoT is a Copy number Transformer autoencoder that leverages multi-head attention mechanism to explore correlations between different genomic regions, and thus capture global features to create latent embeddings for the cells. CoT makes it convenient to first infer cell subpopulations based on the learned embeddings, and then estimate single-cell copy numbers through joint analysis of read counts data for the cells belonging to the same cluster. This exploitation of clonal substructure information in copy number analysis helps to alleviate the effect of read counts non-uniformity, and yield robust estimations of the tumor copy numbers. Performance evaluation on synthetic and real datasets showcases that CoT outperforms the state of the arts, and is highly useful for deciphering clonal copy number substructure.

Detection of rare mutations, copy number alterations, and methylation in the same template DNA molecules.

The analysis of cell-free DNA (cfDNA) from plasma offers great promise for the earlier detection of cancer. At present, changes in DNA sequence, methylation, or copy number are the most sensitive ways to detect the presence of cancer. To further increase the sensitivity of such assays with limited amounts of sample, it would be useful to be able to evaluate the same template molecules for all these changes. Here, we report an approach, called MethylSaferSeqS, that achieves this goal, and can be applied to any standard library preparation method suitable for massively parallel sequencing. The innovative step was to copy both strands of each DNA-barcoded molecule with a primer that allows the subsequent separation of the original strands (retaining their 5-methylcytosine residues) from the copied strands (in which the 5-methylcytosine residues are replaced with unmodified cytosine residues). The epigenetic and genetic alterations present in the DNA molecules can then be obtained from the original and copied strands, respectively. We applied this approach to plasma from 265 individuals, including 198 with cancers of the pancreas, ovary, lung, and colon, and found the expected patterns of mutations, copy number alterations, and methylation. Furthermore, we could determine which original template DNA molecules were methylated and/or mutated. MethylSaferSeqS should be useful for addressing a variety of questions relating genetics and epigenetics.

The repertoire of copy number alteration signatures in human cancer.

Copy number alterations (CNAs) are a predominant source of genetic alterations in human cancer and play an important role in cancer progression. However comprehensive understanding of the mutational processes and signatures of CNA is still lacking. Here we developed a mechanism-agnostic method to categorize CNA based on various fragment properties, which reflect the consequences of mutagenic processes and can be extracted from different types of data, including whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) array. The 14 signatures of CNA have been extracted from 2778 pan-cancer analysis of whole genomes WGS samples, and further validated with 10 851 the cancer genome atlas SNP array dataset. Novel patterns of CNA have been revealed through this study. The activities of some CNA signatures consistently predict cancer patients' prognosis. This study provides a repertoire for understanding the signatures of CNA in cancer, with potential implications for cancer prognosis, evolution and etiology.

rcCAE: a convolutional autoencoder method for detecting intra-tumor heterogeneity and single-cell copy number alterations.

Intra-tumor heterogeneity (ITH) is one of the major confounding factors that result in cancer relapse, and deciphering ITH is essential for personalized therapy. Single-cell DNA sequencing (scDNA-seq) now enables profiling of single-cell copy number alterations (CNAs) and thus aids in high-resolution inference of ITH. Here, we introduce an integrated framework called rcCAE to accurately infer cell subpopulations and single-cell CNAs from scDNA-seq data. A convolutional autoencoder (CAE) is employed in rcCAE to learn latent representation of the cells as well as distill copy number information from noisy read counts data. This unsupervised representation learning via the CAE model makes it convenient to accurately cluster cells over the low-dimensional latent space, and detect single-cell CNAs from enhanced read counts data. Extensive performance evaluations on simulated datasets show that rcCAE outperforms the existing CNA calling methods, and is highly effective in inferring clonal architecture. Furthermore, evaluations of rcCAE on two real datasets demonstrate that it is able to provide a more refined clonal structure, of which some details are lost in clonal inference based on integer copy numbers.

Cracking the pattern of tumor evolution based on single-cell copy number alterations.

Copy number alterations (CNAs) are a key characteristic of tumor development and progression. The accumulation of various CNAs during tumor development plays a critical role in driving tumor evolution. Heterogeneous clones driven by distinct CNAs have different selective advantages, leading to differential patterns of tumor evolution that are essential for developing effective cancer therapies. Recent advances in single-cell sequencing technology have enabled genome-wide copy number profiling of tumor cell populations at single-cell resolution. This has made it possible to explore the evolutionary patterns of CNAs and accurately discover the mechanisms of intra-tumor heterogeneity. Here, we propose a two-step statistical approach that distinguishes neutral, linear, branching and punctuated evolutionary patterns for a tumor cell population based on single-cell copy number profiles. We assessed our approach using a variety of simulated and real single-cell genomic and transcriptomic datasets, demonstrating its high accuracy and robustness in predicting tumor evolutionary patterns. We applied our approach to single-cell DNA sequencing data from 20 breast cancer patients and observed that punctuated evolution is the dominant evolutionary pattern in breast cancer. Similar conclusions were drawn when applying the approach to single-cell RNA sequencing data obtained from 132 various cancer patients. Moreover, we found that differential immune cell infiltration is associated with specific evolutionary patterns. The source code of our study is available at https://github.com/FangWang-SYSU/PTEM.

LINE-1 expression in cancer correlates with p53 mutation, copy number alteration, and S phase checkpoint.

Retrotransposons are genomic DNA sequences that copy themselves to new genomic locations via RNA intermediates; LINE-1 is the only active and autonomous retrotransposon in the human genome. The mobility of LINE-1 is largely repressed in somatic tissues but is derepressed in many cancers, where LINE-1 retrotransposition is correlated with p53 mutation and copy number alteration (CNA). In cell lines, inducing LINE-1 expression can cause double-strand breaks (DSBs) and replication stress. Reanalyzing multiomic data from breast, ovarian, endometrial, and colon cancers, we confirmed correlations between LINE-1 expression, p53 mutation status, and CNA. We observed a consistent correlation between LINE-1 expression and the abundance of DNA replication complex components, indicating that LINE-1 may also induce replication stress in human tumors. In endometrial cancer, high-quality phosphoproteomic data allowed us to identify the DSB-induced ATM-MRN-SMC S phase checkpoint pathway as the primary DNA damage response (DDR) pathway associated with LINE-1 expression. Induction of LINE-1 expression in an in vitro model led to increased phosphorylation of MRN complex member RAD50, suggesting that LINE-1 directly activates this pathway.

Inferring single-cell copy number profiles through cross-cell segmentation of read counts.

BACKGROUND: Copy number alteration (CNA) is one of the major genomic variations that frequently occur in cancers, and accurate inference of CNAs is essential for unmasking intra-tumor heterogeneity (ITH) and tumor evolutionary history. Single-cell DNA sequencing (scDNA-seq) makes it convenient to profile CNAs at single-cell resolution, and thus aids in better characterization of ITH. Despite that several computational methods have been proposed to decipher single-cell CNAs, their performance is limited in either breakpoint detection or copy number estimation due to the high dimensionality and noisy nature of read counts data. RESULTS: By treating breakpoint detection as a process to segment high dimensional read count sequence, we develop a novel method called DeepCNA for cross-cell segmentation of read count sequence and per-cell inference of CNAs. To cope with the difficulty of segmentation, an autoencoder (AE) network is employed in DeepCNA to project the original data into a low-dimensional space, where the breakpoints can be efficiently detected along each latent dimension and further merged to obtain the final breakpoints. Unlike the existing methods that manually calculate certain statistics of read counts to find breakpoints, the AE model makes it convenient to automatically learn the representations. Based on the inferred breakpoints, we employ a mixture model to predict copy numbers of segments for each cell, and leverage expectation-maximization algorithm to efficiently estimate cell ploidy by exploring the most abundant copy number state. Benchmarking results on simulated and real data demonstrate our method is able to accurately infer breakpoints as well as absolute copy numbers and surpasses the existing methods under different test conditions. DeepCNA can be accessed at: https://github.com/zhyu-lab/deepcna . CONCLUSIONS: Profiling single-cell CNAs based on deep learning is becoming a new paradigm of scDNA-seq data analysis, and DeepCNA is an enhancement to the current arsenal of computational methods for investigating cancer genomics.

Concomitant genomic features stratify prognosis to patients with advanced EGFR mutant lung cancer.

This study aimed to explore the clinical significance of genomics features including tumor mutation burden (TMB) and copy number alteration (CNA) for advanced EGFR mutant lung cancer. We retrospectively identified 1378 patients with advanced EGFR mutant lung cancer and next-generation sequencing tests from three cohorts. Multiple co-occurring genomics alternations occurred in a large proportion (97%) of patients with advanced EGFR mutant lung cancers. Both TMB and CNA were predictive biomarkers for these patients. A joint analysis of TMB and CNA found that patients with high TMB and high CNA showed worse responses to EGFR-TKIs and predicted worse outcomes. TMBhighCNAhigh, as a high-risk genomic feature, showed predictive ability in most of the subgroups based on clinical characteristics. These patients had larger numbers of metastatic sites, and higher rates of EGFR copy number amplification, TP53 mutations, and cell-cycle gene alterations, which showed more potential survival gain from combination treatment. Furthermore, a nomogram based on genomic features and clinical features was developed to distinguish prognosis. Genomic features could stratify prognosis and guide clinical treatment for patients with advanced EGFR mutant lung cancer.

Comprehensive Genomic Analysis of Cemento-Ossifying Fibroma.

Cemento-ossifying fibroma (COF) of the jaws is currently classified as a benign mesenchymal odontogenic tumor, and only targeted approaches have been used to assess its genetic alterations. A minimal proportion of COFs harbor CDC73 somatic mutations, and copy number alterations (CNAs) involving chromosomes 7 and 12 have recently been reported in a small proportion of cases. However, the genetic background of COFs remains obscure. We used a combination of whole-exome sequencing and RNA sequencing to assess somatic mutations, fusion transcripts, and CNAs in a cohort of 12 freshly collected COFs. No recurrent fusions have been identified among the 5 cases successfully analyzed by RNA sequencing, with in-frame fusions being detected in 2 cases (MARS1::GOLT1B and PARG::BMS1 in one case and NCLN::FZR1 and NFIC::SAMD1 in the other case) and no candidate fusions identified for the remaining 3 cases. No recurrent pathogenic mutations were detected in the 11 cases that had undergone whole-exome sequencing. A KRAS p.L19F missense variant was detected in one case, and 2 CDC73 deletions were detected in another case. The other variants were of uncertain significance and included variants in PC, ACTB, DOK6, HACE1, and COL1A2 and previously unreported variants in PTPN14, ATP5F1C, APOBEC1, HDAC5, ATF7IP, PARP2, and ACTR3B. The affected genes do not clearly converge on any signaling pathway. CNAs were detected in 5/11 cases (45%), with copy gains involving chromosome 12 occurring in 3/11 cases (27%). In conclusion, no recurrent fusions or pathogenic variants have been detected in the present COF cohort, with copy gains involving chromosome 12 occurring in 27% of cases.

BiTSC 2: Bayesian inference of tumor clonal tree by joint analysis of single-cell SNV and CNA data.

The rapid development of single-cell DNA sequencing (scDNA-seq) technology has greatly enhanced the resolution of tumor cell profiling, providing an unprecedented perspective in characterizing intra-tumoral heterogeneity and understanding tumor progression and metastasis. However, prominent algorithms for constructing tumor phylogeny based on scDNA-seq data usually only take single nucleotide variations (SNVs) as markers, failing to consider the effect caused by copy number alterations (CNAs). Here, we propose BiTSC$^2$, Bayesian inference of Tumor clonal Tree by joint analysis of Single-Cell SNV and CNA data. BiTSC$^2$ takes raw reads from scDNA-seq as input, accounts for the overlapping of CNA and SNV, models allelic dropout rate, sequencing errors and missing rate, as well as assigns single cells into subclones. By applying Markov Chain Monte Carlo sampling, BiTSC$^2$ can simultaneously estimate the subclonal scCNA and scSNV genotype matrices, subclonal assignments and tumor subclonal evolutionary tree. In comparison with existing methods on synthetic and real tumor data, BiTSC$^2$ shows high accuracy in genotype recovery, subclonal assignment and tree reconstruction. BiTSC$^2$ also performs robustly in dealing with scDNA-seq data with low sequencing depth and variant missing rate. BiTSC$^2$ software is available at https://github.com/ucasdp/BiTSC2.

Mutational landscape of inflammatory breast cancer.

BACKGROUND: Inflammatory breast cancer (IBC) is the most pro-metastatic form of BC. Better understanding of its enigmatic pathophysiology is crucial. We report here the largest whole-exome sequencing (WES) study of clinical IBC samples. METHODS: We retrospectively applied WES to 54 untreated IBC primary tumor samples and matched normal DNA. The comparator samples were 102 stage-matched non-IBC samples from TCGA. We compared the somatic mutational profiles, spectra and signatures, copy number alterations (CNAs), HRD and heterogeneity scores, and frequencies of actionable genomic alterations (AGAs) between IBCs and non-IBCs. The comparisons were adjusted for the molecular subtypes. RESULTS: The number of somatic mutations, TMB, and mutational spectra were not different between IBCs and non-IBCs, and no gene was differentially mutated or showed differential frequency of CNAs. Among the COSMIC signatures, only the age-related signature was more frequent in non-IBCs than in IBCs. We also identified in IBCs two new mutational signatures not associated with any environmental exposure, one of them having been previously related to HIF pathway activation. Overall, the HRD score was not different between both groups, but was higher in TN IBCs than TN non-IBCs. IBCs were less frequently classified as heterogeneous according to heterogeneity H-index than non-IBCs (21% vs 33%), and clonal mutations were more frequent and subclonal mutations less frequent in IBCs. More than 50% of patients with IBC harbored at least one high-level of evidence (LOE) AGA (OncoKB LOE 1-2, ESCAT LOE I-II), similarly to patients with non-IBC. CONCLUSIONS: We provide the largest mutational landscape of IBC. Only a few subtle differences were identified with non-IBCs. The most clinically relevant one was the higher HRD score in TN IBCs than in TN non-IBCs, whereas the most intriguing one was the smaller intratumor heterogeneity of IBCs.

Genomic alterations in ovarian endometriosis and subsequently diagnosed ovarian carcinoma.

STUDY QUESTION: Can the alleged association between ovarian endometriosis and ovarian carcinoma be substantiated by genetic analysis of endometriosis diagnosed prior to the onset of the carcinoma? SUMMARY ANSWER: The data suggest that ovarian carcinoma does not originate from ovarian endometriosis with a cancer-like genetic profile; however, a common precursor is probable. WHAT IS KNOWN ALREADY: Endometriosis has been implicated as a precursor of ovarian carcinoma based on epidemiologic studies and the discovery of common driver mutations in synchronous disease at the time of surgery. Endometrioid ovarian carcinoma and clear cell ovarian carcinoma are the most common endometriosis-associated ovarian carcinomas (EAOCs). STUDY DESIGN, SIZE, DURATION: The pathology biobanks of two university hospitals in Sweden were scrutinized to identify women with surgically removed endometrioma who subsequently developed ovarian carcinoma (1998-2016). Only 45 archival cases with EAOC and previous endometriosis were identified and after a careful pathology review, 25 cases were excluded due to reclassification into non-EAOC (n = 9) or because ovarian endometriosis could not be confirmed (n = 16). Further cases were excluded due to insufficient endometriosis tissue or poor DNA quality in either the endometriosis, carcinoma, or normal tissue (n = 9). Finally 11 cases had satisfactory DNA from all three locations and were eligible for further analysis. PARTICIPANTS/MATERIALS, SETTING, METHODS: Epithelial cells were collected from formalin-fixed and paraffin-embedded (FFPE) sections by laser capture microdissection (endometrioma n = 11) or macrodissection (carcinoma n = 11) and DNA was extracted. Normal tissue from FFPE sections (n = 5) or blood samples collected at cancer diagnosis (n = 6) were used as the germline controls for each included patient. Whole-exome sequencing was performed (n = 33 samples). Somatic variants (single-nucleotide variants, indels, and copy number alterations) were characterized, and mutational signatures and kataegis were assessed. Microsatellite instability and mismatch repair status were confirmed with PCR and immunohistochemistry, respectively. MAIN RESULTS AND THE ROLE OF CHANCE: The median age for endometriosis surgery was 42 years, and 54 years for the subsequent ovarian carcinoma diagnosis. The median time between the endometriosis and ovarian carcinoma was 10 (7-30) years. The data showed that all paired samples harbored one or more shared somatic mutations. Non-silent mutations in cancer-associated genes were frequent in endometriosis; however, the same mutations were never observed in subsequent carcinomas. The degree of clonal dominance, demonstrated by variant allele frequency, showed a positive correlation with the time to cancer diagnosis (Spearman's rho 0.853, P < 0.001). Mutations in genes associated with immune escape were the most conserved between paired samples, and regions harboring these genes were frequently affected by copy number alterations in both sample types. Mutational burdens and mutation signatures suggested faulty DNA repair mechanisms in all cases. LARGE SCALE DATA: Datasets are available in the supplementary tables. LIMITATIONS, REASONS FOR CAUTION: Even though we located several thousands of surgically removed endometriomas between 1998 and 2016, only 45 paired samples were identified and even fewer, 11 cases, were eligible for sequencing. The observed high level of intra- and inter-heterogeneity in both groups (endometrioma and carcinoma) argues for further studies of the alleged genetic association. WIDER IMPLICATIONS OF THE FINDINGS: The observation of shared somatic mutations in all paired samples supports a common cellular origin for ovarian endometriosis and ovarian carcinoma. However, contradicting previous conclusions, our data suggest that cancer-associated mutations in endometriosis years prior to the carcinoma were not directly associated with the malignant transformation. Rather, a resilient ovarian endometriosis may delay tumorigenesis. Furthermore, the data indicate that genetic alterations affecting the immune response are early and significant events. STUDY FUNDING/COMPETING INTEREST(S): The present work has been funded by the Sjöberg Foundation (2021-01145 to K.S.; 2022-01-11:4 to A.S.), Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement (965552 to K.S.; 40615 to I.H.; 965065 to A.S.), Swedish Cancer Society (21-1848 to K.S.; 21-1684 to I.H.; 22-2080 to A.S.), BioCARE-A Strategic Research Area at Lund University (I.H. and S.W.-F.), Mrs Berta Kamprad's Cancer Foundation (FBKS-2019-28, I.H.), Cancer and Allergy Foundation (10381, I.H.), Region Västra Götaland (A.S.), Sweden's Innovation Agency (2020-04141, A.S.), Swedish Research Council (2021-01008, A.S.), Roche in collaboration with the Swedish Society of Gynecological Oncology (S.W.-F.), Assar Gabrielsson Foundation (FB19-86, C.M.), and the Lena Wäpplings Foundation (C.M.). A.S. declares stock ownership and is also a board member in Tulebovaasta, SiMSen Diagnostics, and Iscaff Pharma. A.S. has also received travel support from EMBL, Precision Medicine Forum, SLAS, and bioMCC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Validation of a targeted next-generation sequencing panel for pancreatic ductal adenocarcinomas.

Pancreatic ductal adenocarcinoma (PDAC) is reported to be amongst the cancers with the lowest survival rate at 5 years. In the present study we aimed to validate a targeted next-generation sequencing (tNGS) panel to use in clinical routine, investigating genes important for PDAC diagnostic, prognostic and potential theragnostic aspect. In this NGS panel we also designed target regions to inquire about loss of heterozygosity (LOH) of chromosome 18 that has been described to be possibly linked to a worse disease progression. Copy number alteration has also been explored for a subset of genes. The last two methods are not commonly used for routine diagnostic with tNGS panels and we investigated their possible contribution to better characterize PDAC. A series of 140 formalin-fixed paraffin-embedded (FFPE) PDAC samples from 140 patients was characterized using this panel. Ninety-two % of patients showed alterations in at least one of the investigated genes (most frequent KRAS, TP53, SMAD4, CDKN2A and RNF43). Regarding LOH evaluation, we were able to detect chr18 LOH starting at 20% cell tumor percentage. The presence of LOH on chr18 is associated with a worse disease- and metastasis-free survival, in uni- and multivariate analyses. The present study validates the use of a tNGS panel for PDAC characterization, also evaluating chr18 LOH status for prognostic stratification.

The molecular profile of gastric intraepithelial foveolar type neoplasia based on somatic copy number alterations and multiple mutation analysis.

BACKGROUND: Gastric foveolar type neoplasia is a rare histological variant of gastric tumors. It is very difficult to differentiate between benign and malignant intraepithelial foveolar neoplasia (IFN). Although limited molecular alterations have been identified in IFNs, somatic copy number alterations (SCNAs), which are linked to tumor progression, have not been systematically evaluated in IFN. METHODS: The aim of the present study was to comprehensively examine SCNAs using a SNP array in 37 cases of IFN, compared with intestinal type dysplasia, including 39 low grade (LGD) and 32 high grade dysplasia (HGD) cases. In addition, gene mutations were evaluated using a gene panel. Finally, we attempted to determine molecular profiles using a hierarchical clustering analysis. RESULTS: Two patterns could be categorized according to the SCNAs in 108 tumors examined: high (subgroup 1) and low (subgroup 2) frequencies of SCNAs. Although IFN and LGD were associated with subgroup 2, HGD was found in both subgroups. The median numbers of total SCNAs and copy number gains were higher in IFN or HGD than in LGD. In addition, the IFN genotype was characterized by altered genes located at 4p13-4q35.2, including RAP1GDS1 and LEF1, which may be associated with IFN development. Finally, no significant mutations were found in IFNs using a gene panel. CONCLUSIONS: The current molecular profiles of IFN may help elucidate the mechanisms of IFN development.

POFUT1 and PLAGL2 are characteristic markers of mucinous colorectal cancer associated with MUC2 expression.

Colorectal mucinous adenocarcinoma (MAC) is one of the most lethal histological types of colorectal cancer, and its mechanism of development is not well understood. In this study, we aimed to clarify the molecular characteristics of MAC via in silico analysis using The Cancer Genome Atlas database. The expression of genes on chromosome 20q (Chr20q) was negatively associated with the expression of MUC2, which is a key molecule that can be used to distinguish between MAC and nonmucinous adenocarcinoma (NMAC). This was consistent with a significant difference in copy number alteration of Chr20q between the two histological types. We further identified 475 differentially expressed genes (DEGs) between MAC and NMAC, and some of the Chr20q genes among the DEGs are considered to be pivotal genes used to define MAC. Both in vitro and in vivo analysis showed that simultaneous knockdown of POFUT1 and PLAGL2, both of which are located on Chr20q, promoted MUC2 expression. Moreover, these genes were highly expressed in NMAC but not in MAC according to the results of immunohistological studies using human samples. In conclusion, POFUT1 and PLAGL2 are considered to be important for defining MAC, and these genes are associated with MUC2 expression.

A hidden Markov modeling approach for identifying tumor subclones in next-generation sequencing studies.

Allele-specific copy number alteration (ASCNA) analysis is for identifying copy number abnormalities in tumor cells. Unlike normal cells, tumor cells are heterogeneous as a combination of dominant and minor subclones with distinct copy number profiles. Estimating the clonal proportion and identifying mainclone and subclone genotypes across the genome are important for understanding tumor progression. Several ASCNA tools have recently been developed, but they have been limited to the identification of subclone regions, and not the genotype of subclones. In this article, we propose subHMM, a hidden Markov model-based approach that estimates both subclone region and region-specific subclone genotype and clonal proportion. We specify a hidden state variable representing the conglomeration of clonal genotype and subclone status. We propose a two-step algorithm for parameter estimation, where in the first step, a standard hidden Markov model with this conglomerated state variable is fit. Then, in the second step, region-specific estimates of the clonal proportions are obtained by maximizing region-specific pseudo-likelihoods. We apply subHMM to study renal cell carcinoma datasets in The Cancer Genome Atlas. In addition, we conduct simulation studies that show the good performance of the proposed approach. The R source code is available online at https://dceg.cancer.gov/tools/analysis/subhmm. Expectation-Maximization algorithm; Forward-backward algorithm; Somatic copy number alteration; Tumor subclones.

Targeted deep sequencing reveals genomic alterations of actinic keratosis/cutaneous squamous cell carcinoma in situ and cutaneous squamous cell carcinoma.

Actinic keratosis (AK) and cutaneous squamous cell carcinoma in situ (CIS) are two of the most common precursors of cutaneous squamous cell carcinoma (cSCC). However, the genomic landscape of AK/CIS and the drivers of cSCC progression remain to be elucidated. The aim of our study was to investigate the genomic alterations between AK/CIS and cSCC in terms of somatic mutations and copy number alterations (CNAs). We performed targeted deep sequencing of 160 cancer-related genes with a median coverage of 515× for AK (N = 9), CIS (N = 9), cSCC lesions (N = 13), and matched germline controls from 17 patients. cSCC harboured higher abundance of total mutations, driver mutations and CNAs than AK/CIS. Driver mutations were found in TP53 (81%), NOTCH1 (32%), RB1 (26%) and CDKN2A (19%). All AK/CIS and cSCC lesions (93.5%), except two, harboured TP53 or NOTCH1 mutations, some of which were known oncogenic mutations or reported mutations in normal skin. RB1 driver mutations were found in CIS/cSCC (36.4%) but not in AK. CDKN2A driver mutations were found more frequently in cSCC (30.8%) than in AK/CIS (11.1%). Among recurrent (≥3 samples) CNAs (gain in MYC and PIK3CA/SOX2/TP63; loss in CDKN2A and RB1), MYC (8q) gain and CDKN2A (9p) loss were more frequently detected in cSCC (30.8%) than in AK/CIS (11.1%). Ultraviolet was responsible for the majority of somatic mutations in both AK/CIS and cSCC. Our study revealed that AK/CIS lesions harbour prevalent TP53 or NOTCH1 mutations and that additional somatic mutations and CNAs may lead to cSCC progression in AK/CIS lesions.

Genomic landscape of follicular lymphoma across a wide spectrum of clinical behaviors.

While some follicular lymphoma (FL) patients do not require treatment or experience prolonged responses, others relapse early, and little is known about genetic alterations specific to patients with a particular clinical behavior. We selected 56 grade 1-3A FL patients according to their need of treatment or timing of relapse: never treated (n = 7), non-relapsed (19), late relapse (14), early relapse or POD24 (11), and primary refractory (5). We analyzed 56 diagnostic and 12 paired relapse lymphoid tissue biopsies and performed copy number alteration (CNA) analysis and next generation sequencing (NGS). We identified six focal driver losses (1p36.32, 6p21.32, 6q14.1, 6q23.3, 9p21.3, 10q23.33) and 1p36.33 copy-neutral loss of heterozygosity (CN-LOH). By integrating CNA and NGS results, the most frequently altered genes/regions were KMT2D (79%), CREBBP (67%), TNFRSF14 (46%) and BCL2 (40%). Although we found that mutations in PIM1, FOXO1 and TMEM30A were associated with an adverse clinical behavior, definitive conclusions cannot be drawn, due to the small sample size. We identified common precursor cells harboring early oncogenic alterations of the KMT2D, CREBBP, TNFRSF14 and EP300 genes and 16p13.3-p13.2 CN-LOH. Finally, we established the functional consequences of mutations by means of protein modeling (CD79B, PLCG2, PIM1, MCL1 and IRF8). These data expand the knowledge on the genomics behind the heterogeneous FL population and, upon replication in larger cohorts, could contribute to risk stratification and the development of targeted therapies.

Potential Association of Cytochrome P450 Copy Number Alteration in Tumour with Chemotherapy Resistance in Lung Adenocarcinoma Patients.

Resistance to anticancer agents is a major obstacle to efficacious tumour therapy and responsible for high cancer-related mortality rates. Some resistance mechanisms are associated with pharmacokinetic variability in anticancer drug exposure due to genetic polymorphisms of drug-metabolizing cytochrome P450 (CYP) enzymes, whereas variations in tumoural metabolism as a consequence of CYP copy number alterations are assumed to contribute to the selection of resistant cells. A high-throughput quantitative polymerase chain reaction (qPCR)-based method was developed for detection of CYP copy number alterations in tumours, and a scoring system improved the identification of inappropriate reference genes that underwent deletion/multiplication in tumours. The copy numbers of both the target (CYP2C8, CYP3A4) and the reference genes (ALB, B2M, BCKDHA, F5, CD36, MPO, TBP, RPPH1) established in primary lung adenocarcinoma by the qPCR-based method were congruent with those determined by next-generation sequencing (for 10 genes, slope = 0.9498, r2 = 0.72). In treatment naïve adenocarcinoma samples, the copy number multiplication of paclitaxel-metabolizing CYP2C8 and/or CYP3A4 was more prevalent in non-responder patients with progressive disease/exit than in responders with complete remission. The high-throughput qPCR-based method can become an alternative approach to next-generation sequencing in routine clinical practice, and identification of altered CYP copy numbers may provide a promising biomarker for therapy-resistant tumours.