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.
Key Points• Oligo-or monoclonal expansion of HTLV-1-infected T cells in asymptomatic carriers predicts the onset of ATL.• Progression to acute type from indolent ATL was observed only in cases with monoclonal expansion.
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 TCRβ 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.
BackgroundClonal 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.MethodsAs a comprehensively interdisciplinary project, this study combined three main aspects: wet laboratory experiments, in silico analysis and empirical modeling.ResultsWe 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.ConclusionsThrough 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-016-0241-2) contains supplementary material, which is available to authorized users.
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.
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