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Cancer-linked mutations A large-scale genetic analysis of more than 400 breast, lung, ovarian and prostate cancer samples has identified thousands of cancer-related mutations. Kan et al . analysed DNA from cancer patients and identified 2,576 somatic mutations across 1,507 coding genes. Of these, 77 are thought to be significantly mutated, implying possible pathogenic roles for protein kinases, G protein-coupled receptors and other potential therapeutic targets. These authors performed a large-scale study in which they identified 2,576 somatic mutations across 1,507 coding genes from 441 breast, lung, ovarian and prostate cancer types and subtypes. The study provides an overview of the mutational spectra across major human cancers, implies an expanded role for Gα subunits in multiple cancer types and identifies several potential therapeutic targets. The systematic characterization of somatic mutations in cancer genomes is essential for understanding the disease and for developing targeted therapeutics 1 . Here we report the identification of 2,576 somatic mutations across ∼1,800 megabases of DNA representing 1,507 coding genes from 441 tumours comprising breast, lung, ovarian and prostate cancer types and subtypes. We found that mutation rates and the sets of mutated genes varied substantially across tumour types and subtypes. Statistical analysis identified 77 significantly mutated genes including protein kinases, G-protein-coupled receptors such as GRM8 , BAI3 , AGTRL1 (also called APLNR ) and LPHN3 , and other druggable targets. Integrated analysis of somatic mutations and copy number alterations identified another 35 significantly altered genes including GNAS , indicating an expanded role for gα subunits in multiple cancer types. Furthermore, our experimental analyses demonstrate the functional roles of mutant GNAO1 (a Gα subunit) and mutant MAP2K4 (a member of the JNK signalling pathway) in oncogenesis. Our study provides an overview of the mutational spectra across major human cancers and identifies several potential therapeutic targets.

The most recent WHO classification of endocrine and neuroendocrine tumours has brought about significant changes in the diagnosis and grading of these lesions. For instance, pathologists now have the ability to stratify subsets of thyroid and adrenal neoplasms using various histological features and composite risk assessment models. Moreover, novel recommendations on how to approach endocrine neoplasia involve additional immunohistochemical analyses, and the recognition and implementation of these key markers is essential for modernising diagnostic capabilities. Additionally, an improved understanding of tumour origin has led to the renaming of several entities, resulting in the emergence of terminology not yet universally recognised. The adjustments in nomenclature and prognostication may pose a challenge for the clinical team, and care providers might be eager to engage in a dialogue with the diagnosing pathologist, as treatment guidelines have not fully caught up with these recent changes. Therefore, it is crucial for a surgical pathologist to be aware of the knowledge behind the implementation of changes in the WHO classification scheme. This review article will delve into the most significant diagnostic and prognostic changes related to lesions in the parathyroid, thyroid, adrenal glands and the gastroenteropancreatic neuroendocrine system. Additionally, the author will briefly share his personal reflections on the clinical implementation, drawing from a couple of years of experience with these new algorithms.

Reconstructing the evolutionary history of metastases is critical for understanding their basic biological principles and has profound clinical implications. Genome-wide sequencing data has enabled modern phylogenomic methods to accurately dissect subclones and their phylogenies from noisy and impure bulk tumour samples at unprecedented depth. However, existing methods are not designed to infer metastatic seeding patterns. Here we develop a tool, called Treeomics, to reconstruct the phylogeny of metastases and map subclones to their anatomic locations. Treeomics infers comprehensive seeding patterns for pancreatic, ovarian, and prostate cancers. Moreover, Treeomics correctly disambiguates true seeding patterns from sequencing artifacts; 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumour heterogeneity among distinct samples. In silico benchmarking on simulated tumour phylogenies across a wide range of sample purities (15–95%) and sequencing depths (25-800 × ) demonstrates the accuracy of Treeomics compared with existing methods. Tumours frequently metastasize to multiple anatomical sites and understanding how these different metastases evolve may be important for therapy. Here, the authors develop a method—Treeomics—that can construct phylogenies from multiple metastases from next-generation sequencing data.

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications. Sarcomas are morphologically heterogeneous tumours rendering their classification challenging. Here the authors developed a classifier using DNA methylation data from several soft tissue and bone sarcoma subtypes, which has the potential to improve classification for research and clinical purposes.

Background/AimsTo validate the prognostic performance of the American Joint Committee on Cancer (AJCC) eighth edition classification for ocular adnexal lymphoma (OAL).MethodsWe performed a retrospective review of 140 consecutive patients treated for primary OAL between March 2010 and September 2017. Associations between T/N/M categories at presentation and disease-related outcomes, including relapse, progression-free survival (PFS) and overall survival (OS) were evaluated.ResultsSeventy-nine women and 61 men (median age, 52 (range 20–84) years; median follow-up, 57 (range 7–131) months) were included. Histological subtypes included mucosa-associated lymphoid tissue lymphoma (92.1%, n=129), diffuse large B-cell lymphoma (5.0%, n=7), follicular lymphoma (1.4%, n=2) and mantle cell lymphoma (1.4%, n=2). Patients with ≥T2 disease had significantly higher risks of overall relapse (unadjusted HR)=4.32, p=0.016), decreased PFS (uHR=5.19, p=0.004) and decreased OS (uHR=9.21, p=0.047). Patients with ≥N1 disease had significantly higher risks of overall relapse (uHR=9.17, p<0.001) and decreased PFS (uHR=9.24, p<0.001). M1 disease was significantly associated with higher risks of overall relapse (uHR=3.62, p=0.036), decreased PFS (uHR=5.13, p=0.001) and decreased OS (uHR=9.24, p=0.013). On considering TNM categories as continuous data, the uHRs for per level increase in T, N and M categories were 1.77, 1.83 and 2.30 for overall relapse and 1.72, 1.87 and 2.78 for decreased PFS, respectively (p<0.05 for each comparison).ConclusionThe T, N and M categories of the AJCC eighth edition classification have prognostic value for relapse and survival among patients with primary OAL. Particularly, nodal/metastatic involvement at presentation indicated less favourable outcome.

Myxoid pleomorphic liposarcoma is a recently defined subtype of liposarcoma, which preferentially involves the mediastinum of young patients and shows mixed histological features of conventional myxoid liposarcoma and pleomorphic liposarcoma. While myxoid pleomorphic liposarcoma is known to lack the EWSR1/FUS-DDIT3 fusions characteristic of the former, additional genetic data are limited. To further understand this tumor type, we extensively examined a series of myxoid pleomorphic liposarcomas by fluorescence in situ hybridization (FISH), shallow whole genome sequencing (sWGS) and genome-wide DNA methylation profiling. The 12 tumors occurred in 6 females and 6 males, ranging from 17 to 58 years of age (mean 33 years, median 35 years), and were located in the mediastinum (n = 5), back, neck, cheek and leg, including thigh. Histologically, all cases consisted of relatively, bland, abundantly myxoid areas with a prominent capillary vasculature, admixed with much more cellular and less myxoid foci containing markedly pleomorphic spindled cells, numerous pleomorphic lipoblasts and elevated mitotic activity. Using sWGS, myxoid pleomorphic liposarcomas were found to have complex chromosomal alterations, including recurrent large chromosomal gains involving chromosomes 1, 6–8, 18–21 and losses involving chromosomes 13, 16 and 17. Losses in chromosome 13, in particular loss in 13q14 (including RB1, RCTB2, DLEU1, and ITM2B genes), were observed in 4 out of 8 cases analyzed. Additional FISH analyses confirmed the presence of a monoallelic RB1 deletion in 8/12 cases. Moreover, nuclear Rb expression was deficient in all studied cases. None showed DDIT3 gene rearrangement or MDM2 gene amplification. Using genome-wide DNA methylation profiling, myxoid pleomorphic liposarcomas and conventional pleomorphic liposarcomas formed a common methylation cluster, which segregated from conventional myxoid liposarcomas. While the morphologic, genetic and epigenetic characteristics of myxoid pleomorphic liposarcoma suggest a link with conventional pleomorphic liposarcoma, its distinctive clinical features support continued separate classification for the time being.

Background In transitioning from the 7th edition of the tumor‐node‐metastasis classification (TNM‐7) to the 8th edition (TNM‐8), colorectal cancer with peritoneal metastasis was newly categorized as M1c. In the 9th edition of the Japanese Classification of colorectal, appendiceal, and anal carcinoma (JPC‐9), M1c is further subdivided into M1c1 (without other organ involvement) and M1c2 (with other organ involvement). This study aimed to compare the model fit and discriminatory ability of the M category of these three classification systems, as no study to date has made this comparison. Methods The study population consisted of stage IV colorectal cancer patients who were referred to the National Cancer Center Hospital from 2000 to 2017. The Akaike information criterion (AIC), Harrell's concordance index (C‐index), and time‐dependent receiver operating characteristic (ROC) curves were used to compare the three classification systems. Subgroup analyses, stratified by initial treatment year, were also performed. Results According to TNM‐8, 670 (55%) patients had M1a, 273 (22%) had M1b, and 279 (23%) had M1c (87 M1c1 and 192 M1c2 using JPC‐9) tumors. Among the three classification systems, JPC‐9 had the lowest AIC value (JPC‐9: 10546.3; TNM‐7: 10555.9; TNM‐8: 10585.5), highest C‐index (JPC‐9: 0.608; TNM‐7: 0.598; TNM‐8: 0.599), and superior time‐dependent ROC curves throughout the observation period. Subgroup analyses were consistent with these results. Conclusions While the revised M category definition did not improve model fit and discriminatory ability from TNM‐7 to TNM‐8, further subdivision of M1c in JPC‐9 improved these parameters. These results support further revisions to M1 subcategories in future editions of the TNM classification system. In transitioning from the TNM 7th edition to the TNM 8th edition, colorectal cancer with peritoneal metastasis was newly categorized as M1c, and in the Japanese Classification (9th edition), M1c is further subdivided into M1c1 (without other organ involvement) and M1c2 (with other organ involvement). To date, no study has compared the model fit and discriminatory ability of the M category of these three classification systems. In this study, with 1222 consecutive stage IV colorectal cancer patients, we clearly demonstrated that, by comparing AIC, Harrell's c‐index, and time‐dependent ROC curves between the three classification systems, while the revised M category definition did not improve model fit and discriminatory ability from TNM 7th edition to the TNM 8th edition, further subdivision of M1c in the Japanese Classification improved these parameters.

BACKGROUND:A substantial proportion of cancer-related mortality is attributable to recurrent, not de novo metastatic disease, yet we know relatively little about these patients. To fill this gap, investigators often use administrative codes for secondary malignant neoplasm or chemotherapy to identify recurrent cases in population-based datasets. However, these algorithms have not been validated in large, contemporary, routine care cohorts. OBJECTIVE:To evaluate the validity of secondary malignant neoplasm and chemotherapy codes as indicators of recurrence after definitive local therapy for stage I–III lung, colorectal, breast, and prostate cancer. RESEARCH DESIGN, SUBJECTS, AND MEASURES:We assessed the sensitivity, specificity, and positive predictive value (PPV) of these codes 14 and 60 months after diagnosis using 2 administrative datasets linked with gold-standard recurrence status informationCanCORS/Medicare (diagnoses 2003–2005) and HMO/Cancer Research Network (diagnoses 2000–2005). RESULTS:We identified 929 CanCORS/Medicare patients and 5298 HMO/CRN patients. Sensitivity, specificity, and PPV ranged widely depending on which codes were included and the type of cancer. For patients with lung, colorectal, and breast cancer, the combination of secondary malignant neoplasm and chemotherapy codes was the most sensitive (75%–85%); no code-set was highly sensitive and highly specific. For prostate cancer, no code-set offered even moderate sensitivity (≤19%). CONCLUSIONS:Secondary malignant neoplasm and chemotherapy codes could not identify recurrent cancer without some risk of misclassification. Findings based on existing algorithms should be interpreted with caution. More work is needed to develop a valid algorithm that can be used to characterize outcomes and define patient cohorts for comparative effectiveness research studies.

Carcinosarcoma (CS) of the uterus or ovary is a rare, aggressive and biphasic neoplasm composed of carcinoma and sarcoma elements. Previous genomic studies have identified the driver genes and genomic properties associated with CS. However, there is still no molecular subtyping scheme with clinical relevance for this disease. Here, we sequence 109 CS samples, focusing on 596 genes. We identify four molecular subtypes that resemble those observed in endometrial carcinoma: POLE -mutated, microsatellite instability, copy number high, and copy number low subtypes. These molecular subtypes are linked with DNA repair deficiencies, potential therapeutic strategies, and multiple clinicopathological features, including patient outcomes. Multi-regional comparative sequencing reveals genomic alteration-independent CS cell differentiation. Transcriptome and DNA methylome analyses confirm epithelial-mesenchymal transition as a mechanism of sarcoma differentiation. The current study thus provides therapeutic possibilities for CS as well as clues to understanding the molecular histogenic mechanism of its development. Carcinosarcoma of the ovary or uterus comprises both carcinoma and sarcoma elements. Here, the authors perform a multi -omics study of the disease revealing therapeutic possibilities for this rare and aggressive disease.

Introduction Molecular pathological research has contributed to improving the knowledge of different subtypes of ovarian cancer. In parallel with the implementation of the new FIGO staging classification, the WHO classification was revised. The latter is mainly based on the histopathological findings and defines the actual type of tumor. It has, therefore, also an important impact on prognosis and therapy of the patient. Materials and methods The new WHO Classification of Ovarian Cancer published 2014 by Robert Kurman and co-authors is summarized. The major changes compared to the hitherto existing classification are presented. Results The new classification eliminates the previous focus of mesothelial origin of ovarian cancer. Instead, it features a discussion of tubal carcinogenesis of hereditary and some other high-grade serous carcinomas. The previously assumed pathogenesis pathway may be correct for some, but not for all, serous cancers. The new classification was established to classify ovarian cancer in a more consistent way. The earlier transitional cell type of ovarian cancer has been removed while seromucinous tumors have been added as a new entity. The role of some borderline tumors as one possible step in the progression from benign to invasive lesions is incorporated. The article summarizes the essential updates concerning serous, mucinous, seromucinous, endometrioid, clear-cell, and Brenner tumors. Conclusion The new WHO classification takes into account the recent findings on the origin, pathogenesis, and prognosis of different ovarian cancer subtypes. The tubal origin of hereditary and some non-hereditary high-grade serous cancers is mentioned in contrast to the hitherto theory of mesothelial origin of tumors. Seromucinous tumors represent a new entity.