Single-cell gene regulatory network prediction by explainable AI.

The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.

Molecular characteristics and improved survival prediction in a cohort of 2023 ependymomas.

The diagnosis of ependymoma has moved from a purely histopathological review with limited prognostic value to an integrated diagnosis, relying heavily on molecular information. However, as the integrated approach is still novel and some molecular ependymoma subtypes are quite rare, few studies have correlated integrated pathology and clinical outcome, often focusing on small series of single molecular types. We collected data from 2023 ependymomas as classified by DNA methylation profiling, consisting of 1736 previously published and 287 unpublished methylation profiles. Methylation data and clinical information were correlated, and an integrated model was developed to predict progression-free survival. Patients with EPN-PFA, EPN-ZFTA, and EPN-MYCN tumors showed the worst outcome with 10-year overall survival rates of 56%, 62%, and 32%, respectively. EPN-PFA harbored chromosome 1q gains and/or 6q losses as markers for worse survival. In supratentorial EPN-ZFTA, a combined loss of CDKN2A and B indicated worse survival, whereas a single loss did not. Twelve out of 200 EPN-ZFTA (6%) were located in the posterior fossa, and these tumors relapsed or progressed even earlier than supratentorial tumors with a combined loss of CDKN2A/B. Patients with MPE and PF-SE, generally regarded as non-aggressive tumors, only had a 10-year progression-free survival of 59% and 65%, respectively. For the prediction of the 5-year progression-free survival, Kaplan-Meier estimators based on the molecular subtype, a Support Vector Machine based on methylation, and an integrated model based on clinical factors, CNV data, and predicted methylation scores achieved balanced accuracies of 66%, 68%, and 73%, respectively. Excluding samples with low prediction scores resulted in balanced accuracies of over 80%. In sum, our large-scale analysis of ependymomas provides robust information about molecular features and their clinical meaning. Our data are particularly relevant for rare and hardly explored tumor subtypes and seemingly benign variants that display higher recurrence rates than previously believed.

Transcriptomic and epigenetic dissection of spinal ependymoma (SP-EPN) identifies clinically relevant subtypes enriched for tumors with and without NF2 mutation.

Ependymomas encompass multiple clinically relevant tumor types based on localization and molecular profiles. Tumors of the methylation class "spinal ependymoma" (SP-EPN) represent the most common intramedullary neoplasms in children and adults. However, their developmental origin is ill-defined, molecular data are scarce, and the potential heterogeneity within SP-EPN remains unexplored. The only known recurrent genetic events in SP-EPN are loss of chromosome 22q and NF2 mutations, but neither types and frequency of these alterations nor their clinical relevance have been described in a large, epigenetically defined series. Transcriptomic (n = 72), epigenetic (n = 225), genetic (n = 134), and clinical data (n = 112) were integrated for a detailed molecular overview on SP-EPN. Additionally, we mapped SP-EPN transcriptomes to developmental atlases of the developing and adult spinal cord to uncover potential developmental origins of these tumors. The integration of transcriptomic ependymoma data with single-cell atlases of the spinal cord revealed that SP-EPN display the highest similarities to mature adult ependymal cells. Unsupervised hierarchical clustering of transcriptomic data together with integrated analysis of methylation profiles identified two molecular SP-EPN subtypes. Subtype A tumors primarily carried previously known germline or sporadic NF2 mutations together with 22q loss (bi-allelic NF2 loss), resulting in decreased NF2 expression. Furthermore, they more often presented as multilocular disease and demonstrated a significantly reduced progression-free survival as compared to SP-EP subtype B. In contrast, subtype B predominantly contained samples without NF2 mutation detected in sequencing together with 22q loss (monoallelic NF2 loss). These tumors showed regular NF2 expression but more extensive global copy number alterations. Based on integrated molecular profiling of a large multi-center cohort, we identified two distinct SP-EPN subtypes with important implications for genetic counseling, patient surveillance, and drug development priorities.

Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling.

The complexity of diagnostic (surgical) pathology has increased substantially over the last decades with respect to histomorphological and molecular profiling. Pathology has steadily expanded its role in tumor diagnostics and beyond from disease entity identification via prognosis estimation to precision therapy prediction. It is therefore not surprising that pathology is among the disciplines in medicine with high expectations in the application of artificial intelligence (AI) or machine learning approaches given their capabilities to analyze complex data in a quantitative and standardized manner to further enhance scope and precision of diagnostics. While an obvious application is the analysis of histological images, recent applications for the analysis of molecular profiling data from different sources and clinical data support the notion that AI will enhance both histopathology and molecular pathology in the future. At the same time, current literature should not be misunderstood in a way that pathologists will likely be replaced by AI applications in the foreseeable future. Although AI will transform pathology in the coming years, recent studies reporting AI algorithms to diagnose cancer or predict certain molecular properties deal with relatively simple diagnostic problems that fall short of the diagnostic complexity pathologists face in clinical routine. Here, we review the pertinent literature of AI methods and their applications to pathology, and put the current achievements and what can be expected in the future in the context of the requirements for research and routine diagnostics.

Molecular refinement of pilocytic astrocytoma in adult patients.

AIM: Pilocytic astrocytomas (PA) in adults are rare and may be challenging to identify based only on histomorphology. Compared to their paediatric counterparts, they are reportedly molecularly more diverse and associated with a worse prognosis. We aimed to describe the characteristics of adult PAs more precisely by comprehensively profiling a series of 79 histologically diagnosed adult cases (≥18 years). METHODS: We performed global DNA methylation profiling and DNA and RNA panel sequencing, and integrated the results with clinical data. We further compared the molecular characteristics of adult and paediatric PAs that had a significant match to one of the established PA methylation classes in the Heidelberg brain tumour classifier. RESULTS: The mean age in our cohort was 33 years, and 43% of the tumours were located supratentorially. Based on methylation profiling, only 39% of the cases received a significant match to a PA methylation class. Sixteen per cent matched a different tumour type and 45% had a Heidelberg classifier score <0.9 with an affiliation to diverse established methylation classes in t-SNE analyses. Although the KIAA1549::BRAF fusion was found in 98% of paediatric PAs, this was true for only 27% of histologically defined and 55% of adult PAs defined by methylation profiling. CONCLUSIONS: A particularly high fraction of adult tumours with histological features of PA do not match current PA methylation classes, indicating ambiguous histology and an urgent need for molecular profiling. Moreover, even in adult PAs with a match to a PA methylation class, the distribution of genetic drivers differs significantly from their paediatric counterparts (p<0.01).

Recurrent atypical teratoid/rhabdoid tumors (AT/RT) reveal discrete features of progression on histology, epigenetics, copy number profiling, and transcriptomics.

Atypical teratoid/rhabdoid tumors (AT/RT) are the most common malignant brain tumors manifesting in infancy. They split into four molecular types. The major three (AT/RT-SHH, AT/RT-TYR, and AT/RT-MYC) all carry mutations in SMARCB1, the fourth quantitatively smaller type is characterized by SMARCA4 mutations (AT/RT-SMARCA4). Molecular characteristics of disease recurrence or metastatic spread, which go along with a particularly dismal outcome, are currently unclear. Here, we investigated tumor tissue from 26 patients affected by AT/RT to identify signatures of recurrences in comparison with matched primary tumor samples. Microscopically, AT/RT recurrences demonstrated a loss of architecture and significantly enhanced mitotic activity as compared to their related primary tumors. Based on DNA methylation profiling, primary tumor and related recurrence were grossly similar, but three out of 26 tumors belonged to a different molecular type or subtype after second surgery compared to related primary lesions. Copy number variations (CNVs) differed in six cases, showing novel gains on chromosome 1q or losses of chromosome 10 in recurrences as the most frequent alterations. To consolidate these observations, our cohort was combined with a data set of unmatched primary and recurrent AT/RT, which demonstrated chromosome 1q gain and 10 loss in 18% (n = 7) and 11% (n = 4) of the recurrences (n = 38) as compared to 7% (n = 3) and 0% (n = 0) in the primary tumors (n = 44), respectively. Similar to the observations made by DNA methylation profiling, RNA sequencing of our cohort revealed AT/RT primary tumors and matched recurrences clustering closely together. However, a number of genes showed significantly altered expression in AT/RT-SHH recurrences. Many of them are known tumor driving growth factors, involved in embryonal development and tumorigenesis, or are cell-cycle-associated. Overall, our work identifies subtle molecular changes that occur in the course of the disease and that may help define novel therapeutic targets for AT/RT recurrences.

Identification of low and very high-risk patients with non-WNT/non-SHH medulloblastoma by improved clinico-molecular stratification of the HIT2000 and I-HIT-MED cohorts.

Molecular groups of medulloblastoma (MB) are well established. Novel risk stratification parameters include Group 3/4 (non-WNT/non-SHH) methylation subgroups I-VIII or whole-chromosomal aberration (WCA) phenotypes. This study investigates the integration of clinical and molecular parameters to improve risk stratification of non-WNT/non-SHH MB. Non-WNT/non-SHH MB from the HIT2000 study and the HIT-MED registries were selected based on availability of DNA-methylation profiling data. MYC or MYCN amplification and WCA of chromosomes 7, 8, and 11 were inferred from methylation array-based copy number profiles. In total, 403 non-WNT/non-SHH MB were identified, 346/403 (86%) had a methylation class family Group 3/4 methylation score (classifier v11b6) ≥ 0.9, and 294/346 (73%) were included in the risk stratification modeling based on Group 3 or 4 score (v11b6) ≥ 0.8 and subgroup I-VIII score (mb_g34) ≥ 0.8. Group 3 MB (5y-PFS, survival estimation ± standard deviation: 41.4 ± 4.6%; 5y-OS: 48.8 ± 5.0%) showed poorer survival compared to Group 4 (5y-PFS: 68.2 ± 3.7%; 5y-OS: 84.8 ± 2.8%). Subgroups II (5y-PFS: 27.6 ± 8.2%) and III (5y-PFS: 37.5 ± 7.9%) showed the poorest and subgroup VI (5y-PFS: 76.6 ± 7.9%), VII (5y-PFS: 75.9 ± 7.2%), and VIII (5y-PFS: 66.6 ± 5.8%) the best survival. Multivariate analysis revealed subgroup in combination with WCA phenotype to best predict risk of progression and death. The integration of clinical (age, M and R status) and molecular (MYC/N, subgroup, WCA phenotype) variables identified a low-risk stratum with a 5y-PFS of 94 ± 5.7 and a very high-risk stratum with a 5y-PFS of 29 ± 6.1%. Validation in an international MB cohort confirmed the combined stratification scheme with 82.1 ± 6.0% 5y-PFS in the low and 47.5 ± 4.1% in very high-risk groups, and outperformed the clinical model. These newly identified clinico-molecular low-risk and very high-risk strata, accounting for 6%, and 21% of non-WNT/non-SHH MB patients, respectively, may improve future treatment stratification.

Machine learning models predict the primary sites of head and neck squamous cell carcinoma metastases based on DNA methylation.

In head and neck squamous cell cancers (HNSCs) that present as metastases with an unknown primary (HNSC-CUPs), the identification of a primary tumor improves therapy options and increases patient survival. However, the currently available diagnostic methods are laborious and do not offer a sufficient detection rate. Predictive machine learning models based on DNA methylation profiles have recently emerged as a promising technique for tumor classification. We applied this technique to HNSC to develop a tool that can improve the diagnostic work-up for HNSC-CUPs. On a reference cohort of 405 primary HNSC samples, we developed four classifiers based on different machine learning models [random forest (RF), neural network (NN), elastic net penalized logistic regression (LOGREG), and support vector machine (SVM)] that predict the primary site of HNSC tumors from their DNA methylation profile. The classifiers achieved high classification accuracies (RF = 83%, NN = 88%, LOGREG = SVM = 89%) on an independent cohort of 64 HNSC metastases. Further, the NN, LOGREG, and SVM models significantly outperformed p16 status as a marker for an origin in the oropharynx. In conclusion, the DNA methylation profiles of HNSC metastases are characteristic for their primary sites, and the classifiers developed in this study, which are made available to the scientific community, can provide valuable information to guide the diagnostic work-up of HNSC-CUP. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Clinical and molecular characterization of isolated M1 disease in pediatric medulloblastoma: experience from the German HIT-MED studies.

PURPOSE: To evaluate the clinical impact of isolated spread of medulloblastoma cells into cerebrospinal fluid without additional macroscopic metastases (M1-only). METHODS: The HIT-MED database was searched for pediatric patients with M1-only medulloblastoma diagnosed from 2000 to 2019. Corresponding clinical and molecular data was evaluated. Treatment was stratified by age and changed over time for older patients. RESULTS: 70 patients with centrally reviewed M1-only disease were identified. Clinical data was available for all and molecular data for 45/70 cases. 91% were non-WNT/non-SHH medulloblastoma (Grp3/4). 5-year PFS for 52 patients ≥ 4 years was 59.4 (± 7.1) %, receiving either upfront craniospinal irradiation (CSI) or SKK-sandwich chemotherapy (CT). Outcomes did not differ between these strategies (5-year PFS: CSI 61.7 ± 9.9%, SKK-CT 56.7 ± 6.1%). For patients < 4 years (n = 18), 5-year PFS was 50.0 (± 13.2) %. M1-persistence occurred exclusively using postoperative CT and was a strong negative predictive factor (pPFS/OS < 0.01). Patients with additional clinical or molecular high-risk (HR) characteristics had worse outcomes (5-year PFS 42.7 ± 10.6% vs. 64.0 ± 7.0%, p = 0.03). In n = 22 patients ≥ 4 years with full molecular information and without additional HR characteristics, risk classification by molecular subtyping had an effect on 5-year PFS (HR 16.7 ± 15.2%, SR 77.8 ± 13.9%; p = 0.01). CONCLUSIONS: Our results confirm that M1-only is a high-risk condition, and further underline the importance of CSF staging. Specific risk stratification of affected patients needs attention in future discussions for trials and treatment recommendations. Future patients without contraindications may benefit from upfront CSI by sparing risks related to higher cumulative CT applied in sandwich regimen.

Cauda equina paragangliomas express HOXB13.

HOXB13 is expressed in the tail bud of the developing embryo as well as in cauda equina paragangliomas and in myxopapillary ependymomas. In contrast, pheochromocytomas and paraganglioma in other locations as well as many other tumors occuring in spinal cord regions are negative.

Atypical teratoid/rhabdoid tumors (ATRTs) with SMARCA4 mutation are molecularly distinct from SMARCB1-deficient cases.

Atypical teratoid/rhabdoid tumors (ATRTs) are very aggressive childhood malignancies of the central nervous system. The underlying genetic cause are inactivating bi-allelic mutations in SMARCB1 or (rarely) in SMARCA4. ATRT-SMARCA4 have been associated with a higher frequency of germline mutations, younger age, and an inferior prognosis in comparison to SMARCB1 mutated cases. Based on their DNA methylation profiles and transcriptomics, SMARCB1 mutated ATRTs have been divided into three distinct molecular subgroups: ATRT-TYR, ATRT-SHH, and ATRT-MYC. These subgroups differ in terms of age at diagnosis, tumor location, type of SMARCB1 alterations, and overall survival. ATRT-SMARCA4 are, however, less well understood, and it remains unknown, whether they belong to one of the described ATRT subgroups. Here, we examined 14 ATRT-SMARCA4 by global DNA methylation analyses. We show that they form a separate group segregating from SMARCB1 mutated ATRTs and from other SMARCA4-deficient tumors like small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) or SMARCA4 mutated extra-cranial malignant rhabdoid tumors. In contrast, medulloblastoma (MB) samples with heterozygous SMARCA4 mutations do not group separately, but with established MB subgroups. RNA sequencing of ATRT-SMARCA4 confirmed the clustering results based on DNA methylation profiling and displayed an absence of typical signature genes upregulated in SMARCB1 deleted ATRT. In summary, our results suggest that, in line with previous clinical observations, ATRT-SMARCA4 should be regarded as a distinct molecular subgroup.

Multiclass cancer classification in fresh frozen and formalin-fixed paraffin-embedded tissue by DigiWest multiplex protein analysis.

Histomorphology and immunohistochemistry are the most common ways of cancer classification in routine cancer diagnostics, but often reach their limits in determining the organ origin in metastasis. These cancers of unknown primary, which are mostly adenocarcinomas or squamous cell carcinomas, therefore require more sophisticated methodologies of classification. Here, we report a multiplex protein profiling-based approach for the classification of fresh frozen and formalin-fixed paraffin-embedded (FFPE) cancer tissue samples using the digital western blot technique DigiWest. A DigiWest-compatible FFPE extraction protocol was developed, and a total of 634 antibodies were tested in an initial set of 16 FFPE samples covering tumors from different origins. Of the 303 detected antibodies, 102 yielded significant correlation of signals in 25 pairs of fresh frozen and FFPE primary tumor samples, including head and neck squamous cell carcinomas (HNSC), lung squamous cell carcinomas (LUSC), lung adenocarcinomas (LUAD), colorectal adenocarcinomas (COAD), and pancreatic adenocarcinomas (PAAD). For this signature of 102 analytes (covering 88 total proteins and 14 phosphoproteins), a support vector machine (SVM) algorithm was developed. This allowed for the classification of the tissue of origin for all five tumor types studied here with high overall accuracies in both fresh frozen (90.4%) and FFPE (77.6%) samples. In addition, the SVM classifier reached an overall accuracy of 88% in an independent validation cohort of 25 FFPE tumor samples. Our results indicate that DigiWest-based protein profiling represents a valuable method for cancer classification, yielding conclusive and decisive data not only from fresh frozen specimens but also FFPE samples, thus making this approach attractive for routine clinical applications.

Molecular characterization of CNS paragangliomas identifies cauda equina paragangliomas as a distinct tumor entity.

Paragangliomas/pheochromocytomas are rare neuroendocrine tumors that arise from the adrenal gland or ganglia at various sites throughout the body. They display a remarkable diversity of driver alterations and are associated with germline mutations in up to 40% of the cases. Comprehensive molecular profiling of abdomino-thoracic paragangliomas revealed four molecularly defined and clinically relevant subtypes. Paragangliomas of the cauda equina region are considered to belong to one of the defined molecular subtypes, but a systematic molecular analysis has not yet been performed. In this study, we analyzed genome-wide DNA methylation profiles of 57 cauda equina paragangliomas and show that these tumors are epigenetically distinct from non-spinal paragangliomas and other tumors. In contrast to paragangliomas of other sites, chromosomal imbalances are widely lacking in cauda equina paragangliomas. Furthermore, RNA and DNA exome sequencing revealed that frequent genetic alterations found in non-spinal paragangliomas-including the prognostically relevant SDH mutations-are absent in cauda equina paragangliomas. Histologically, cauda equina paragangliomas show frequently gangliocytic differentiation and strong immunoreactivity to pan-cytokeratin and cytokeratin 18, which is not common in paragangliomas of other sites. None of our cases had a familial paraganglioma syndrome. Tumors rarely recurred (9%) or presented with multiple lesions within the spinal compartment (7%), but did not metastasize outside the CNS. In summary, we show that cauda equina paragangliomas represent a distinct, sporadic tumor entity defined by a unique clinical and morpho-molecular profile.

Proteogenomic systems analysis identifies targeted therapy resistance mechanisms in EGFR-mutated lung cancer.

Cancer precision medicine largely relies on knowledge about genetic aberrations in tumors and next-generation-sequencing studies have shown a high mutational complexity in many cancers. Although a large number of the observed mutations is believed to be not causally linked with cancer, the functional effects of many rare mutations but also of combinations of driver mutations are often unknown. Here, we perform a systems analysis of a model of EGFR-mutated nonsmall cell lung cancer resistant to targeted therapy that integrates whole exome sequencing, global time-course discovery phosphoproteomics and computational modeling to identify functionally relevant molecular alterations. Our approach allows for a complexity reduction from over 2,000 genetic events potentially involved in mediating resistance to only 44 phosphoproteins and 35 topologically close genetic alterations. We perform single- and combination-drug testing against the predicted phosphoproteins and discovered that targeting of HSPB1, DBNL and AKT1 showed potent antiproliferative effects overcoming resistance against EGFR-inhibitory therapy. Our approach may therefore be used to complement mutational profiling to identify functionally relevant molecular aberrations and propose combination therapies across cancers.

TCF4 (E2-2) harbors tumor suppressive functions in SHH medulloblastoma.

The TCF4 gene encodes for the basic helix-loop-helix transcription factor 4 (TCF4), which plays an important role in the development of the central nervous system (CNS). Haploinsufficiency of TCF4 was found to cause Pitt-Hopkins syndrome (PTHS), a severe neurodevelopmental disorder. Recently, the screening of a large cohort of medulloblastoma (MB), a highly aggressive embryonal brain tumor, revealed almost 20% of adult patients with MB of the Sonic hedgehog (SHH) subtype carrying somatic TCF4 mutations. Interestingly, many of these mutations have previously been detected as germline mutations in patients with PTHS. We show here that overexpression of wild-type TCF4 in vitro significantly suppresses cell proliferation in MB cells, whereas mutant TCF4 proteins do not to the same extent. Furthermore, RNA sequencing revealed significant upregulation of multiple well-known tumor suppressors upon expression of wild-type TCF4. In vivo, a prenatal knockout of Tcf4 in mice caused a significant increase in apoptosis accompanied by a decreased proliferation and failed migration of cerebellar granule neuron precursor cells (CGNP), which are thought to be the cells of origin for SHH MB. In contrast, postnatal in vitro and in vivo knockouts of Tcf4 with and without an additional constitutive activation of the SHH pathway led to significantly increased proliferation of CGNP or MB cells. Finally, publicly available data from human MB show that relatively low expression levels of TCF4 significantly correlate with a worse clinical outcome. These results not only point to time-specific roles of Tcf4 during cerebellar development but also suggest a functional linkage between TCF4 mutations and the formation of SHH MB, proposing that TCF4 acts as a tumor suppressor during postnatal stages of cerebellar development.

Immunohistochemical analysis of Bcl-2, nuclear S100A4, MITF and Ki67 for risk stratification of early-stage melanoma - A combined IHC score for melanoma risk stratification.

BACKGROUND AND OBJECTIVES: Overall survival (OS) in patients with early-stage malignant melanoma differs. To date, there are no established prognostic markers. We aimed to contribute to a better understanding of potential prognostic immunohistochemical markers for risk stratification. PATIENTS AND METHODS: 161 surgically resected early-stage malignant melanomas (stage pT1 and pT2) were analyzed for expression of 20 different proteins using immunohistochemistry. The results were correlated with OS. The cohort was randomly split into a discovery and a validation cohort. RESULTS: High Bcl-2 expression, high nuclear S100A4 expression as well as a Ki67 proliferation index of ≥ 20 % were associated with shorter OS. Strong MITF immunoreactivity was a predictor for favorable prognosis. A combination of these four markers resulted in a multi-marker score with significant prognostic value in multivariate survival analysis (HR: 3.704; 95 % CI 1.484 to 9.246; p = 0.005). Furthermore, the score was able to differentiate a low-risk group with excellent OS rates (five-year survival rate: 100 %), an intermediate-risk group (five-year survival rate: 81.8 %) and a high-risk group (five-year survival rate: 52.6 %). The prognostic value was confirmed within the validation cohort. CONCLUSIONS: Combined immunohistochemical analysis of Bcl-2, nuclear S100A4, Ki67 and MITF could contribute to better risk stratification of early-stage malignant melanoma patients.

Mutation patterns in genes encoding interferon signaling and antigen presentation: A pan-cancer survey with implications for the use of immune checkpoint inhibitors.

Blockade of immune checkpoints has become a powerful tool in cancer medicine, which is effective across various solid cancer types and hematologic malignancies. While immunohistochemical detection of PD-L1 expression in tumor cells, immune cells, or both has been introduced as predictive biomarker in several clinical trials, shortcomings and limitations of this approach were quickly recognized. As a single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer, various genetic determinants of therapy success, including microsatellite instability, mutational burden, and PD-L1 amplification, are being investigated. Very recent work indicates that mutations in B2M, JAK1, and JAK2 render melanoma resistant to immune checkpoint blockade, thus serving as negative response predictors. Using the TCGA dataset, we performed a pan-cancer analysis of potentially damaging mutations in key genes implicated in antigen presentation and interferon-gamma signaling and investigated associations with transcript levels of immune checkpoint genes, cytolytic activity, and mutational burden. For B2M, JAK1, and JAK2, we observed overall mutation frequencies of 1.8%, 2%, and 2.6%, respectively, and found significant associations with mutational burden. On pathway level, melanoma as well as bladder, gastric, and lung cancer were most frequently affected by putative resistance mutations with mutation rates of 27%-50% in the antigen presentation pathway and of 16%-21% in the interferon signaling pathway. Our analysis suggests that a significant number of tumors harbor mutations that may negatively interfere with immune checkpoint inhibition, or confer a higher likelihood of resistance for which a second hit is ultimately required. Since these mutations are prevalent in treatment-naïve tumors, genetic screening prior to therapy might complement current approaches at predicting response to immune checkpoint blockade.

Tubular, lactating, and ductal adenomas are devoid of MED12 Exon2 mutations, and ductal adenomas show recurrent mutations in GNAS and the PI3K-AKT pathway.

Adenomas of the breast are rare benign tumors although single cases with malignant behavior have been reported. However, the genetic basis of these tumors is unknown. Employing targeted next generation sequencing of 50 cancer-related genes as well as Sanger sequencing, we profiled a cohort of 18 mammary adenomas comprising 9 ductal, 6 tubular, and 3 lactating adenoma. Missense mutations were detected in 8 of the 18 cases (44%). Specifically, five (56%) ductal adenomas and three (50%) tubular adenomas harbored mutated genes. No mutations were detected in lactating adenomas. Three of the nine ductal adenomas showed mutant AKT1 (p.E17K) with two of them harboring an additional GNAS mutation (p.R201C). One case had mutant PIK3CA (p.H1047R) and another case a mutation in GNAS (p.R201C). The three cases of mutated tubular adenomas showed mutations in either MET or FGFR3. Of note, we did not detect copy number changes and none of the cases including tubular adenomas had mutations in exon 2 of MED12. Our results suggest that ductal adenomas are related to papillomas of the breast and screening for mutations in exon 2 of MED12 might help to facilitate differential diagnosis between tubular adenoma and fibroadenoma in difficult cases. Lastly, our data exemplarily demonstrate that mutations in cancer-related genes per se do not indicate malignancy but occur in benign tumors. © 2016 Wiley Periodicals, Inc.

Mutations in genes encoding PI3K-AKT and MAPK signaling define anogenital papillary hidradenoma.

Papillary hidradenoma (a.k.a. hidradenoma papilliferum) is a benign tumor of the anogenital region that almost exclusively arises in middle-aged Caucasian women. These tumors may recur and rare cases of malignant development have been reported. The genetic basis of papillary hidradenoma is currently unknown. Hence, we employed targeted high-coverage next generation sequencing interrogating 50 cancer-related genes and conventional Sanger sequencing to investigate the mutational landscape in a cohort of 15 cases. Additionally, we analyzed the HPV status of these tumors. Thirteen cases (87%) harbored mutations in cancer-related genes. Recurrent mutations in PIK3CA and AKT1 were present in 10 of the cases (67%). One PIK3CA mutated case had a concomitant STK11 mutation. Three cases harbored mutually exclusive mutations in BRAF, APC and ERBB4. The remaining two cases showed no mutations. None of the cases harbored DNA of human papilloma virus. Our results also provide evidence that--just as BRAF V600E mutations in hyperplastic polyps and benign nevi- a mutated driver gene does not imply malignant behavior per se but may set the basis for malignant transformation. The latter point may explain why rare cases of papillary hidradenoma have been reported to take a malignant course. Lastly, our genetic data may suggest treatment avenues beyond conventional surgery for some of these tumors.