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Copy number alterations (CNAs) play an important role in molding the genomes of breast cancers and have been shown to be clinically useful for prognostic and therapeutic purposes. However, our knowledge of intra-tumoral genetic heterogeneity of this important class of somatic alterations is limited. Here, using single-cell sequencing, we comprehensively map out the facets of copy number alteration heterogeneity in a cohort of breast cancer tumors. Ou/var/www/html/elife/12-05-2020/backup/r analyses reveal: genetic heterogeneity of non-tumor cells (i.e. stroma) within the tumor mass; the extent to which copy number heterogeneity impacts breast cancer genomes and the importance of both the genomic location and dosage of sub-clonal events; the pervasive nature of genetic heterogeneity of chromosomal amplifications; and the association of copy number heterogeneity with clinical and biological parameters such as polyploidy and estrogen receptor negative status. Our data highlight the power of single-cell genomics in dissecting, in its many forms, intra-tumoral genetic heterogeneity of CNAs, the magnitude with which CNA heterogeneity affects the genomes of breast cancers, and the potential importance of CNA heterogeneity in phenomena such as therapeutic resistance and disease relapse., Competing Interests: TB, JK, KV, KM, HC, SD, FA, MR, LR, AL, JS, YM, RS, RG, KC, GN, VV, CC, AK, LH, MW, JH No competing interests declared, JW, ND is an employee of Philips Research North America. AG is affiliated with House Gordon Software Company LTD. The author has no other competing interests to declare.

A bioinformatics approach was employed to identify transcriptome alterations in the peripheral blood mononuclear cells of well-characterized human subjects who were diagnosed with early disseminated Lyme disease (LD) based on stringent microbiological and clinical criteria. Transcriptomes were assessed at the time of presentation and also at approximately 1 month (early convalescence) and 6 months (late convalescence) after initiation of an appropriate antibiotic regimen. Comparative transcriptomics identified 335 transcripts, representing 233 unique genes, with significant alterations of at least 2-fold expression in acute- or convalescent-phase blood samples from LD subjects relative to healthy donors. Acute-phase blood samples from LD subjects had the largest number of differentially expressed transcripts (187 induced, 54 repressed). This transcriptional profile, which was dominated by interferon-regulated genes, was sustained during early convalescence. 6 months after antibiotic treatment the transcriptome of LD subjects was indistinguishable from that of healthy controls based on two separate methods of analysis. Return of the LD expression profile to levels found in control subjects was concordant with disease outcome; 82% of subjects with LD experienced at least one symptom at the baseline visit compared to 43% at the early convalescence time point and only a single patient (9%) at the 6-month convalescence time point. Using the random forest machine learning algorithm, we developed an efficient computational framework to identify sets of 20 classifier genes that discriminated LD from other bacterial and viral infections. These novel LD biomarkers not only differentiated subjects with acute disseminated LD from healthy controls with 96% accuracy but also distinguished between subjects with acute and resolved (late convalescent) disease with 97% accuracy. IMPORTANCE Lyme disease (LD), caused by Borrelia burgdorferi , is the most common tick-borne infectious disease in the United States. We examined gene expression patterns in the blood of individuals with early disseminated LD at the time of diagnosis (acute) and also at approximately 1 month and 6 months following antibiotic treatment. A distinct acute LD profile was observed that was sustained during early convalescence (1 month) but returned to control levels 6 months after treatment. Using a computer learning algorithm, we identified sets of 20 classifier genes that discriminate LD from other bacterial and viral infections. In addition, these novel LD biomarkers are highly accurate in distinguishing patients with acute LD from healthy subjects and in discriminating between individuals with active and resolved infection. This computational approach offers the potential for more accurate diagnosis of early disseminated Lyme disease. It may also allow improved monitoring of treatment efficacy and disease resolution., (Copyright © 2020 Petzke et al.)

Xp11 (TFE3) translocation renal cell carcinoma (RCC) is officially recognized as a distinct subtype of RCC in the 2004 WHO classification. This neoplasm is characterized by several chromosomal translocations between the TFE3-involving Xp11.2 breakpoint and various fusion partners. To date, five partner genes have been identified, that is, PRCC in 1q21, PSF in 1q34, ASPL in 17q25, CLTC in 17q23, and NONO in Xq12; and three additional translocations have been reported with no partner gene being defined: t(X;3)(p11;q23), t(X;10)(p11;q23), and t(X;19)(p11;q13). Here, we report the identification of a novel TFE3 fusion partner, PARP14 in chromosome band3q21. We used RNA-seq on a 10-year-old FFPE (formalin-fixed, paraffin-embedded) tissue sample, which carried t(X;3)(p11;q23) as detected in the original cytogenetic study. The fusion transcript connected the 5'-end of the first two exons of PARP14 to the 3'-end of five exons of TFE3, which was verified by reverse transcription PCR (RT-PCR) and Sanger sequencing. Similar to other TFE3 fusions previously reported, the predicted PARP14-TFE3 product retains the nuclear localization and DNA-binding domains of TFE3. This finding expands the list of TFE3 translocation partner genes and re-emphasizes the essential oncogenic role of TFE3 fusion proteins in this tumor. Our result also clearly demonstrated the feasibility of identifying chromosomal translocation by RNA-seq in clinical FFPE, which are easily accessible and associated with valuable clinical information. © 2015 Wiley Periodicals, Inc., (© 2015 Wiley Periodicals, Inc.)