David Steiner, MD

Abstract

Subcutaneous panniculitis-like T-cell lymphoma (SPTCL) is a malignant primary cutaneous T-cell lymphoma that is challenging to distinguish from other neoplastic and reactive panniculitides. In an attempt to identify somatic variants in SPTCL that may be diagnostically or therapeutically relevant, we performed both exome sequencing on paired tumor-normal samples and targeted sequencing of hematolymphoid-malignancy-associated genes on tumor biopsies. Exome sequencing was performed on skin biopsies from 4 cases of skin-limited SPTCL, 1 case of peripheral T-cell lymphoma, not otherwise specified with secondary involvement of the panniculus, and 2 cases of lupus panniculitis. This approach detected between 1 and 13 high-confidence somatic variants that were predicted to result in a protein alteration per case. Variants of interest identified include 1 missense mutation in ARID1B in 1 case of SPTCL. To detect variants that were present at a lower level, we used a more sensitive targeted panel to sequence 41 hematolymphoid-malignancy-associated genes. The targeted panel was applied to 2 of the biopsies that were evaluated by whole exome sequencing as well as 5 additional biopsies. Potentially pathogenic variants were identified in KMT2D and PLCG1 among others, but no gene was altered in >2 of the 7 cases sequenced. One variant that was notably absent from the cases sequences is RHOA G17V. Further work will be required to further elucidate the genetic abnormalities that lead to this rare lymphoma.

View details for DOI 10.1097/PAI.0000000000000683

View details for PubMedID 31702703

Abstract

BackgroundDeep learning has the potential to augment the use of chest radiography in clinical radiology, but challenges include poor generalizability, spectrum bias, and difficulty comparing across studies.PurposeTo develop and evaluate deep learning models for chest radiograph interpretation by using radiologist-adjudicated reference standards.Materials and MethodsDeep learning models were developed to detect four findings (pneumothorax, opacity, nodule or mass, and fracture) on frontal chest radiographs. This retrospective study used two data sets. Data set 1 (DS1) consisted of 759 611 images from a multicity hospital network and ChestX-ray14 is a publicly available data set with 112 120 images. Natural language processing and expert review of a subset of images provided labels for 657 954 training images. Test sets consisted of 1818 and 1962 images from DS1 and ChestX-ray14, respectively. Reference standards were defined by radiologist-adjudicated image review. Performance was evaluated by area under the receiver operating characteristic curve analysis, sensitivity, specificity, and positive predictive value. Four radiologists reviewed test set images for performance comparison. Inverse probability weighting was applied to DS1 to account for positive radiograph enrichment and estimate population-level performance.ResultsIn DS1, population-adjusted areas under the receiver operating characteristic curve for pneumothorax, nodule or mass, airspace opacity, and fracture were, respectively, 0.95 (95% confidence interval [CI]: 0.91, 0.99), 0.72 (95% CI: 0.66, 0.77), 0.91 (95% CI: 0.88, 0.93), and 0.86 (95% CI: 0.79, 0.92). With ChestX-ray14, areas under the receiver operating characteristic curve were 0.94 (95% CI: 0.93, 0.96), 0.91 (95% CI: 0.89, 0.93), 0.94 (95% CI: 0.93, 0.95), and 0.81 (95% CI: 0.75, 0.86), respectively.ConclusionExpert-level models for detecting clinically relevant chest radiograph findings were developed for this study by using adjudicated reference standards and with population-level performance estimation. Radiologist-adjudicated labels for 2412 ChestX-ray14 validation set images and 1962 test set images are provided. RSNA, 2019Online supplemental material is available for this article.See also the editorial by Chang in this issue.

View details for DOI 10.1148/radiol.2019191293

View details for PubMedID 31793848

View details for DOI 10.1097/PGP.0000000000000507

View details for Web of Science ID 000471670300012

View details for DOI 10.1038/s41587-019-0114-2

View details for Web of Science ID 000478028700023

View details for DOI 10.1200/PO.18.00214

View details for Web of Science ID 000473546800001

Abstract

Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells.

View details for PubMedID 31061481

Abstract

The past two decades have seen unprecedented advances in the field of oncogenomics. The ongoing characterization of neoplastic tissues through genomic techniques has transformed many aspects of cancer research, diagnosis, and treatment. However, identifying sequence variants with biological and clinical significance is a challenging endeavor. In order to accomplish this task, variants must be annotated and interpreted using various online resources. Data on protein structure, functional prediction, variant frequency in relevant populations, and multipleother factors have beencompiled in usefuldatabasesfor this purpose.Thus, understanding the available online resourcesfor the annotation and interpretationof sequencevariantsis critical to aid molecular pathologistsand researchers working in this space.

View details for DOI 10.1007/978-3-030-24100-1_7

View details for PubMedID 31713167

Abstract

Colorectal carcinomas (CRC) with mismatch repair (MMR) deficiency have increased tumor mutation burden and respond to immune checkpoint inhibitor therapy. The Cancer Genome Atlas identified hypermutated CRCs with somatic mutations in DNA polymerase (POLE) with mutation burdens exceeding that of MMR-deficient CRCs.To identify the morphologic, immunophenotypic and molecular features of POLE mutated CRCs, 63 consecutive MMR-intact CRCs were evaluated by Sanger sequencing for POLE exonuclease domain mutations in exons 9, 11, 13 and 14 and confirmed by next generation sequencing. Tumor immune microenvironment and immunoscores were assessed in POLE-mutated CRCs using immunohistochemistry to detect CD3+/CD8+ tumor-infiltrating lymphocytes and compared to 59 non-POLE mutated MMR-intact CRC, 10 non-POLE mutated MMR-deficient CRCs, and 223 normal colonic mucosa.4.8% CRC (4 MMR-intact primary and 1 MMR-intact metastasis) harbored POLE mutations in amino acid 286 in exon 9 (p.P286R) or exon 13 (p.V411L). POLE mutated CRCs arose in the transverse colon and rectum, were male-predominant, younger, and showed increased tumor-infiltrating lymphocytes and immune cells at the tumor-stromal interface. The patient with metastatic POLE mutated CRC was placed on PD-1 inhibitor treatment with marked and sustained response. These data indicate that POLE mutated CRCs have hypermutated phenotypes despite MMR-intact status, with mutation burdens higher than that in microsatellite unstable CRCs. Given the recent approval for treatment of microsatellite unstable cancer with immune checkpoint inhibitors, assessment of POLE status may help guide therapeutic decisions for hypermutated tumors with intact MMR that would otherwise be missed by routine testing.

View details for DOI 10.1111/his.13984

View details for PubMedID 31479159

View details for Web of Science ID 000444677800226

Abstract

Low-grade serous carcinomas only rarely coexist with or progress to high-grade tumors. We present a case of low-grade serous carcinoma with transformation to carcinosarcoma on recurrence in the lymph node. Identical BRAF V600E and telomerase reverse transcriptase promoter mutations were identified in both the original and recurrent tumor. Given that telomerase reverse transcriptase promotor mutations are thought to play a role in progression of other tumor types, the function of telomerase reverse transcriptase mutations in BRAF mutated low-grade serous carcinoma deserves investigation.

View details for PubMedID 29620581

View details for Web of Science ID 000429308604486

View details for Web of Science ID 000459341003278

Abstract

Cancer somatic mutations can generate neoantigens that distinguish malignant from normal cells. However, the personalized identification and validation of neoantigens remains a major challenge. Here we discover neoantigens in human mantle-cell lymphomas by using an integrated genomic and proteomic strategy that interrogates tumour antigen peptides presented by major histocompatibility complex (MHC) class I and class II molecules. We applied this approach to systematically characterize MHC ligands from 17 patients. Remarkably, all discovered neoantigenic peptides were exclusively derived from the lymphoma immunoglobulin heavy- or light-chain variable regions. Although we identified MHC presentation of private polymorphic germline alleles, no mutated peptides were recovered from non-immunoglobulin somatically mutated genes. Somatic mutations within the immunoglobulin variable region were almost exclusively presented by MHC class II. We isolated circulating CD4(+) T cells specific for immunoglobulin-derived neoantigens and found these cells could mediate killing of autologous lymphoma cells. These results demonstrate that an integrative approach combining MHC isolation, peptide identification, and exome sequencing is an effective platform to uncover tumour neoantigens. Application of this strategy to human lymphoma implicates immunoglobulin neoantigens as targets for lymphoma immunotherapy.

View details for DOI 10.1038/nature21433

View details for PubMedID 28329770

View details for Web of Science ID 000393724400686

View details for PubMedID 29180504

View details for PubMedID 29180505

View details for Web of Science ID 000394446803106

Abstract

MicroRNA (miRNA)-deficient helper Tcells exhibit abnormal IFN- production and decreased proliferation. However, the contributions of individual miRNAs to this phenotype remain poorly understood. We conducted a screen for miRNA function in primary Tcells and identified individual miRNAs that rescue the defects associated with miRNA deficiency. Multiple members of the miR-17 and miR-92 families enhanced miRNA-deficient Tcell proliferation whereas miR-29 largely corrected their aberrant interferon- (IFN-) expression. Repression of IFN- production by miR-29 involved direct targeting of both T-bet and Eomes, two transcription factors known to induce IFN- production. Although not usually expressed at functionally relevant amounts in helper Tcells, Eomes was abundant in miRNA-deficient cells and was upregulated after miR-29 inhibition in wild-type cells. These results demonstrate that miR-29 regulates helper Tcell differentiation by repressing multiple target genes, including at least two that are independently capable of inducing the T helper 1 (Th1) cell gene expression program.

View details for DOI 10.1016/j.immuni.2011.07.009

View details for Web of Science ID 000294387900008

View details for PubMedID 21820330

View details for PubMedCentralID PMC3361370