Iron deposition and increased alveolar septal capillary density in nonfibrotic lung tissue are associated with pulmonary hypertension in idiopathic pulmonary fibrosis

<p>Abstract</p> <p>Background</p> <p>Early diagnosis of pulmonary hypertension (PH) in idiopathic pulmonary fibrosis (IPF) has potential prognostic and therapeutic implications but can be difficult due to the lack of specific clinical manifestations or accurate non-invasive tests. Histopathologic parameters correlating with PH in IPF are also not known. Remodeling of postcapillary pulmonary vessels has been reported in the nonfibrotic areas of explanted lungs from IPF patients. We hypothesized that iron deposition and increased alveolar capillaries, the findings often seen in postcapillary PH, might predict the presence of clinical PH, independent of the severity of fibrosis or ventilatory dysfunction in IPF patients. To test this hypothesis, we examined the association between these histologic parameters and the degree of PH, with consideration of the severity of disease in IPF.</p> <p>Methods</p> <p>Iron deposition and alveolar septal capillary density (ASCD) were evaluated on histologic sections with hematoxylin-eosin, iron, elastin and CD34 stainings. Percentage of predicted forced vital capacity (FVC%) was used for grading pulmonary function status. Fibrosis score assessed on high resolution computed tomography (HRCT) was used for evaluating overall degree of fibrosis in whole lungs. Right ventricular systolic pressure (RVSP) by transthoracic echocardiography was used for the estimation of PH. Univariate and multivariate regression analyses were performed.</p> <p>Results</p> <p>A cohort of 154 patients was studied who had the clinicopathological diagnosis of IPF with surgical lung biopsies or explants during the period of 1997 to 2006 at Mayo Clinic Rochester. In univariate analysis, RVSP in our IPF cases was associated with both iron deposition and ASCD (p < 0.001). In multivariate analysis with FVC% and HRCT fibrosis score included, iron deposition (p = 0.02), but not ASCD (p = 0.076), maintained statistically significant association with RVSP. FVC% was associated with RVSP on univariate analysis but not on multivariate analysis, while fibrosis score lacked any association with RVSP by either univariate or multivariate analyses.</p> <p>Conclusions</p> <p>Iron deposition and ASCD in non fibrotic lung tissue showed an association with RVSP, suggesting that these features are possible morphologic predictors of PH in IPF.</p

A Prospective Correlation of Tissue Histopathology With Nucleic Acid Yield in Metastatic Castration-Resistant Prostate Cancer Biopsy Specimens

Objective: To determine histopathologic, exome, and transcriptome nucleic acid material yield from prospectively collected metastatic tissue biopsy specimens in patients with metastatic castration-resistant prostate cancer (mCRPC). Patients and Methods: Patients with mCRPC initiating abiraterone acetate therapy underwent 2 serial metastatic site core needle biopsies after study activation on May 17, 2013. Multiple cores were obtained, and from each core, 1- to 2-mm segments were separated and formalin fixed for histopathologic examination. Tumor purity was determined for DNA and RNA from the rest of the biopsy specimen. RNA quality was assessed by calculation of an RNA integrity number and a DV200 score. Results: A total of 89 patients underwent 172 uniformly processed core needle biopsies (89 on visit 1 and 83 on visit 2) between May 30, 2013, and September 10, 2015. Metastatic sites biopsied included bone (131), lymph nodes (31), liver (5), lung (3), and pelvic soft tissues (2). Of the 172 biopsy specimens, 85 (49%) had at least one of the multiple cores positive for tumor on histopathologic examination (53 of 88 [60%] from visit 1 and 32 of 83 [39%] from visit 2; P=.006). Metastatic carcinoma was observed in 50 of 130 bone lesion specimens (38%), compared to 35 of 41 nonbone specimens (85%) (P<.001). More than 10% tumoral DNA purity was observed in 89% and 80% of visit 1 and visit 2 biopsy specimens, respectively. Similarly, more than 10% tumor RNA purity was observed in 79% of visit 1 vs 59% for visit 2 (P=.008). In all, 134 of 172 procedures (78%) yielded tumor material either by histopathologic or nucleic acid purity analysis. Conclusion: This study found that biopsy specimens from mCRPC sites yield adequate histopathologic, exome, and transcriptome material in most, but not all, cases. This finding has relevance for future genome sequencing studies on the introduction of targeted therapeutic agents. Trial Registration: clinicaltrials.gov Identifier: 01953640

Mutational Landscapes of Sequential Prostate Metastases and Matched Patient Derived Xenografts during Enzalutamide Therapy

<div><p>Developing patient derived models from individual tumors that capture the biological heterogeneity and mutation landscape in advanced prostate cancer is challenging, but essential for understanding tumor progression and delivery of personalized therapy in metastatic castrate resistant prostate cancer stage. To demonstrate the feasibility of developing patient derived xenograft models in this stage, we present a case study wherein xenografts were derived from cancer metastases in a patient progressing on androgen deprivation therapy and prior to initiating pre-chemotherapy enzalutamide treatment. Tissue biopsies from a metastatic rib lesion were obtained for sequencing before and after initiating enzalutamide treatment over a twelve-week period and also implanted subcutaneously as well as under the renal capsule in immuno-deficient mice. The genome and transcriptome landscapes of xenografts and the original patient tumor tissues were compared by performing whole exome and transcriptome sequencing of the metastatic tumor tissues and the xenografts at both time points. After comparing the somatic mutations, copy number variations, gene fusions and gene expression we found that the patient’s genomic and transcriptomic alterations were preserved in the patient derived xenografts with high fidelity. These xenograft models provide an opportunity for predicting efficacy of existing and potentially novel drugs that is based on individual metastatic tumor expression signature and molecular pharmacology for delivery of precision medicine.</p></div

Comparison of genome and transcriptome landscapes between patient tumor tissue and patient derived xenograft models (PDXs).

<p>(A) Recall (grey) and precision (tan) of detected somatic mutations. Recall = number of somatic mutations called from both patient tissue and xenograft divided by total somatic mutations called from patient tissue; Precision = number of somatic mutations called from both patient tissue and xenograft divided by total mutations called from xenograft. (B) Heatmap showing the concordance of relative allele frequency of somatic mutations between patient tissues and xenografts. Rows correspond to patients and xenograft samples and columns correspond to 60 selected somatic mutations. (C) Circos plot showing profiles of copy number variation for patient tumor tissues and xenografts. From outside to inside, tracks correspond to 1 = V1/met, 1A = V1/xeno/A, 1B = V1/xeno/B, 1C = V1/xeno/C, 1AA = V1/xeno/A/A, 1BA = V1/xeno/B/A, V1/xeno/C/A, V2/met, V2/xeno/A, V2/xeno/A/A1, V2/xeno/A/A2 and V2/xeno/A/B. (D) Pair-wise gene expression correlation between patient tissues and xenografts. Correlation was measured by Pearson correlation coefficient. Gene expression was measured by log10 (RPKM, Reads Per Kilobase exon per Million mapped reads).</p

Classification of xenograft whole exome sequencing (panels A and B) and RNA sequencing reads (panels C and D).

<p>Reads generated from xenograft samples were divided into five groups including “graft”, “host”, “both”, “neither” and “ambiguous” using tool developed by Conway et al. (A) Reads assignments for 10 xenograft whole exome sequencing data. (B) Average proportion of whole exome sequencing reads assigned to the 5 groups mentioned above. (C) Reads assignments for 10 xenograft RNA-seq data. (D) Average proportion of RNA-seq reads assigned to the 5 groups mentioned above.</p