Mutational signatures reveal mutual exclusivity of homologous recombination and mismatch repair deficiencies in colorectal and stomach tumors.

Decomposing somatic mutation spectra into mutational signatures and their corresponding etiologies provides a powerful approach for investigating the mechanism of DNA damage and repair. Assessing microsatellite (in)stability (MSI/MSS) status and interpreting their clinical relevance in different malignancies offers significant diagnostic and prognostic value. However, little is known about microsatellite (in)stability and its interactions with other DNA repair mechanisms such as homologous recombination (HR) in different cancer types. Based on whole-genome/exome mutational signature analysis, we showed HR deficiency (HRd) and mismatch repair deficiency (MMRd) occur in a significantly mutually exclusive manner in stomach and colorectal adenocarcinomas. ID11 signature with currently unknown etiology was prevalent in MSS tumors, co-occurred with HRd and was mutually exclusive with MMRd. Apolipoprotein B mRNA editing enzyme, Catalytic polypeptide-like (APOBEC) signature co-occurred with HRd and was mutually exclusive with MMRd in stomach tumors. The HRd signature in MSS tumors and the MMRd signature in MSI tumors were the first or second dominant signatures wherever detected. HRd may drive a distinct subgroup of MSS tumors and lead to poor clinical outcome. These analyses offer insight into mutational signatures in MSI and MMS tumors and reveal opportunities for improved clinical diagnosis and personalized treatment of MSS tumors.

Mutational signatures reveal ternary relationships between homologous recombination repair, APOBEC, and mismatch repair in gynecological cancers.

BACKGROUND: Revealing the impacts of endogenous and exogenous mutagenesis processes is essential for understanding the etiology of somatic genomic alterations and designing precise prognostication and treatment strategies for cancer. DNA repair deficiency is one of the main sources of endogenous mutagenesis and is increasingly recognized as a target for cancer therapeutics. The role and prevalence of mechanisms that underly different forms of DNA repair deficiencies and their interactions remain to be elucidated in gynecological malignancies. METHODS: We analyzed 1231 exomes and 268 whole-genomes from three major gynecological malignancies including uterine corpus endometrial carcinoma (UCEC) as well as ovarian and cervical cancers. We also analyzed data from 134 related cell lines. We extracted and compared de novo and refitted mutational signature profiles using complementary and confirmatory approaches and performed interaction analysis to detect co-occurring and mutually exclusive signatures. RESULTS: We found an inverse relationship between homologous recombination deficiency (HRd) and mismatch repair deficiency (MMRd). Moreover, APOBEC co-occurred with HRd but was mutually exclusive with MMRd. UCEC tumors were dominated by MMRd, yet a subset of them manifested the HRd and APOBEC signatures. Conversely, ovarian tumors were dominated by HRd, while a subset represented MMRd and APOBEC. In contrast to both, cervical tumors were dominated by APOBEC with a small subsets showing the POLE, HRd, and MMRd signatures. Although the type, prevalence, and heterogeneity of mutational signatures varied across the tumor types, the patterns of co-occurrence and exclusivity were consistently observed in all. Notably, mutational signatures in gynecological tumor cell lines reflected those detected in primary tumors. CONCLUSIONS: Taken together, these analyses indicate that application of mutation signature analysis not only advances our understanding of mutational processes and their interactions, but also it has the potential to stratify patients that could benefit from treatments available for tumors harboring distinct mutational signatures and to improve clinical decision-making for gynecological malignancies.

The genomic landscape of metastatic histologic special types of invasive breast cancer.

Histologic special types of breast cancer (BC) account for ~20% of BCs. Large sequencing studies of metastatic BC have focused on invasive ductal carcinomas of no special type (IDC-NSTs). We sought to define the repertoire of somatic genetic alterations of metastatic histologic special types of BC. We reanalyzed targeted capture sequencing data of 309 special types of BC, including metastatic and primary invasive lobular carcinomas (ILCs; n = 132 and n = 127, respectively), mixed mucinous (n = 5 metastatic and n = 14 primary), micropapillary (n = 12 metastatic and n = 8 primary), and metaplastic BCs (n = 6 metastatic and n = 5 primary), and compared metastatic histologic special types of BC to metastatic IDC-NSTs matched according to clinicopathologic characteristics and to primary special type BCs. The genomic profiles of metastatic and primary special types of BC were similar. Important differences, however, were noted: metastatic ILCs harbored a higher frequency of genetic alterations in TP53, ESR1, FAT1, RFWD2, and NF1 than primary ILCs, and in CDH1, PIK3CA, ERBB2, TBX3, NCOR1, and RFWD2 than metastatic IDC-NSTs. Metastatic ILCs displayed a higher mutational burden, and more frequently dominant APOBEC mutational signatures than primary ILCs and matched metastatic IDC-NSTs. ESR1 and NCOR mutations were frequently detected in metastatic mixed mucinous BCs, whereas PIK3CA and TP53 were the most frequently altered genes in metastatic micropapillary and metaplastic BCs, respectively. Taken together, primary and metastatic BCs histologic special types have remarkably similar repertoires of somatic genetic alterations. Metastatic ILCs more frequently harbor APOBEC mutational signatures than primary ILCs and metastatic IDC-NSTs.

RNA sequencing identifies clonal structure of T-cell repertoires in patients with adult T-cell leukemia/lymphoma.

The diversity of T-cell receptor (TCR) repertoires, as generated by somatic DNA rearrangements, is central to immune system function. High-throughput sequencing technologies now allow examination of antigen receptor repertoires at single-nucleotide and, more recently, single-cell resolution. The TCR repertoire can be altered in the context of infections, malignancies or immunological disorders. Here we examined the diversity of TCR clonality and its association with pathogenesis and prognosis in adult T-cell leukemia/lymphoma (ATL), a malignancy caused by infection with human T-cell leukemia virus type-1 (HTLV-1). We analyzed 62 sets of high-throughput RNA sequencing data from 59 samples of HTLV-1-infected individuals-asymptomatic carriers (ACs), smoldering, chronic, acute and lymphoma ATL subtypes-and three uninfected controls to evaluate TCR distribution. Based on these TCR profiles, CD4-positive cells and ACs showed polyclonal patterns, whereas ATL patients showed oligo- or monoclonal patterns (with 446 average clonotypes across samples). Expression of TCRα and TCRß genes in the dominant clone differed among the samples. ACs, CD4-positive samples and smoldering patients showed significantly higher TCR diversity compared with chronic, acute and lymphoma subtypes. CDR3 sequence length distribution, amino acid conservation and gene usage variability for ATL patients resembled those of peripheral blood cells from ACs and healthy donors. Thus, determining monoclonal architecture and clonal diversity by RNA sequencing might be useful for prognostic purposes and for personalizing ATL diagnosis and assessment of treatments.

Mutational Intratumor Heterogeneity is a Complex and Early Event in the Development of Adult T-cell Leukemia/Lymphoma.

The clonal architecture of tumors plays a vital role in their pathogenesis and invasiveness; however, it is not yet clear how this clonality contributes to different malignancies. In this study we sought to address mutational intratumor heterogeneity (ITH) in adult T-cell leukemia/lymphoma (ATL). ATL is a malignancy with an incompletely understood molecular pathogenesis caused by infection with human T-cell leukemia virus type-1 (HTLV-1). To determine the clonal structure through tumor genetic diversity profiles, we investigated 142 whole-exome sequencing data of tumor and matched normal samples from 71 ATL patients. Based on SciClone analysis, the ATL samples showed a wide spectrum of modes over clonal/subclonal frequencies ranging from one to nine clusters. The average number of clusters was six across samples, but the number of clusters differed among different samples. Of these ATL samples, 94% had more than two clusters. Aggressive ATL cases had slightly more clonal clusters than indolent types, indicating the presence of ITH during earlier stages of disease. The known significantly mutated genes in ATL were frequently clustered together and possibly coexisted in the same clone. IRF4, CCR4, TP53, and PLCG1 mutations were almost clustered in subclones with a moderate variant allele frequency (VAF), whereas HLA-B, CARD11, and NOTCH1 mutations were clustered in subclones with lower VAFs. Taken together, these results show that ATL displays a high degree of ITH and a complex subclonal structure. Our findings suggest that clonal/subclonal architecture might be a useful measure for prognostic purposes and personalized assessment of the therapeutic response.

Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization.

BACKGROUND: Human T cell leukemia virus type 1 (HTLV-1) causes adult T cell leukemia (ATL) in a proportion of infected individuals after a long latency period. Development of ATL is a multistep clonal process that can be investigated by monitoring the clonal expansion of HTLV-1-infected cells by isolation of provirus integration sites. The clonal composition (size, number, and combinations of clones) during the latency period in a given infected individual has not been clearly elucidated. METHODS: We used high-throughput sequencing technology coupled with a tag system for isolating integration sites and measuring clone sizes from 60 clinical samples. We assessed the role of clonality and clone size dynamics in ATL onset by modeling data from high-throughput monitoring of HTLV-1 integration sites using single- and multiple-time-point samples. RESULTS: From four size categories analyzed, we found that big clones (B; 513-2048 infected cells) and very big clones (VB; >2048 infected cells) had prognostic value. No sample harbored two or more VB clones or three or more B clones. We examined the role of clone size, clone combination, and the number of integration sites in the prognosis of infected individuals. We found a moderate reverse correlation between the total number of clones and the size of the largest clone. We devised a data-driven model that allows intuitive representation of clonal composition. CONCLUSIONS: This integration site-based clonality tree model represents the complexity of clonality and provides a global view of clonality data that facilitates the analysis, interpretation, understanding, and visualization of the behavior of clones on inter- and intra-individual scales. It is fully data-driven, intuitively depicts the clonality patterns of HTLV-1-infected individuals and can assist in early risk assessment of ATL onset by reflecting the prognosis of infected individuals. This model should assist in assimilating information on clonal composition and understanding clonal expansion in HTLV-1-infected individuals.

Multidisciplinary insight into clonal expansion of HTLV-1-infected cells in adult T-cell leukemia via modeling by deterministic finite automata coupled with high-throughput sequencing.

BACKGROUND: Clonal expansion of leukemic cells leads to onset of adult T-cell leukemia (ATL), an aggressive lymphoid malignancy with a very poor prognosis. Infection with human T-cell leukemia virus type-1 (HTLV-1) is the direct cause of ATL onset, and integration of HTLV-1 into the human genome is essential for clonal expansion of leukemic cells. Therefore, monitoring clonal expansion of HTLV-1-infected cells via isolation of integration sites assists in analyzing infected individuals from early infection to the final stage of ATL development. However, because of the complex nature of clonal expansion, the underlying mechanisms have yet to be clarified. Combining computational/mathematical modeling with experimental and clinical data of integration site-based clonality analysis derived from next generation sequencing technologies provides an appropriate strategy to achieve a better understanding of ATL development. METHODS: As a comprehensively interdisciplinary project, this study combined three main aspects: wet laboratory experiments, in silico analysis and empirical modeling. RESULTS: We analyzed clinical samples from HTLV-1-infected individuals with a broad range of proviral loads using a high-throughput methodology that enables isolation of HTLV-1 integration sites and accurate measurement of the size of infected clones. We categorized clones into four size groups, "very small", "small", "big", and "very big", based on the patterns of clonal growth and observed clone sizes. We propose an empirical formal model based on deterministic finite state automata (DFA) analysis of real clinical samples to illustrate patterns of clonal expansion. CONCLUSIONS: Through the developed model, we have translated biological data of clonal expansion into the formal language of mathematics and represented the observed clonality data with DFA. Our data suggest that combining experimental data (absolute size of clones) with DFA can describe the clonality status of patients. This kind of modeling provides a basic understanding as well as a unique perspective for clarifying the mechanisms of clonal expansion in ATL.

Clonality of HTLV-1-infected T cells as a risk indicator for development and progression of adult T-cell leukemia.

Adult T-cell leukemia (ATL) is an aggressive T-cell malignancy caused by human T-cell leukemia virus type 1 (HTLV-1) that develops along a carcinogenic process involving 5 or more genetic events in infected cells. The lifetime incidence of ATL among HTLV-1-infected individuals is approximately 5%. Although epidemiologic studies have revealed risk factors for ATL, the molecular mechanisms that determine the fates of carriers remain unclear. A better understanding of clonal composition and related longitudinal dynamics would clarify the process of ATL leukemogenesis and provide insights into the mechanisms underlying the proliferation of a malignant clone. Genomic DNA samples and clinical information were obtained from individuals enrolled in the Joint Study for Predisposing Factors for ATL Development, a Japanese prospective cohort study. Forty-seven longitudinal samples from 20 individuals (9 asymptomatic carriers and 11 patients with ATL at enrollment) were subjected to a clonality analysis. A method based on next-generation sequencing was used to characterize clones on the basis of integration sites. Relationships were analyzed among clonal patterns, clone sizes, and clinical status, including ATL onset and progression. Among carriers, those exhibiting an oligoclonal or monoclonal pattern with largely expanded clones subsequently progressed to ATL. All indolent patients who progressed to acute-type ATL exhibited monoclonal expansion. In both situations, the major expanded clone after progression was derived from the largest pre-existing clone. This study has provided the first detailed information regarding the dynamics of HTLV-1-infected T-cell clones and collectively suggests that the clonality of HTLV-1-infected cells could be a useful predictive marker of ATL onset and progression.