Optimization of Fluorescence In Situ Hybridization Protocols in the Era of Precision Medicine.

Fluorescence in situ hybridization (FISH) is a cytogenetic assay that is widely used in both clinical and research settings to validate genetic aberrations. Simple in principle, it is based on denaturation and hybridization of a DNA probe and its complementary sequence; however, it is subject to continuous optimization. Here we share how in-house FISH can be optimized using different control tissues to visualize and ultimately validate common and novel genetic abnormalities unearthed by next-generation sequencing (NGS). Seven specific FISH probes were designed and labeled, and conditions for eight tissue types and one patient-derived tumor organoid were optimized. Formalin-fixed paraffin-embedded (FFPE) tissue slides were used for each experiment. Slides were first deparaffinized, then placed in a pretreatment solution followed by a digestion step. In-house FISH probes were then added to the tissue to be denatured and hybridized, and then washed twice. To obtain optimal results, probe concentration, pepsin incubation time, denaturation, and the two post-hybridization washes were optimized for each sample. By modifying the above conditions, all FISH experiments were optimized in separate tissue types to investigate specific genomic alterations in tumors arising in those tissues. Signals were clear and distinct, allowing for visualization of the selected probes. Following this protocol, our lab has quickly optimized 11 directly labeled in-house FISH probes to support genetic aberrations nominated by NGS, including most recent discoveries through whole-genome sequencing analyses. We describe a robust approach of how to advance in-house labeled FISH probes. By following these guidelines, reliable and reproducible FISH results can be obtained to interrogate FFPE slides from benign, tumor tissues, and patient-derived tumor organoid specimens. This is of most relevance in the era of NGS and precision oncology. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Metaphase FISH optimization Support Protocol 1: In-house probe labeling and preparation Support Protocol 2: Metaphase spread preparation Basic Protocol 2: Optimization of FISH on formalin-fixed paraffin-embedded tissue.

Weakly-supervised tumor purity prediction from frozen H&E stained slides.

BACKGROUND: Estimating tumor purity is especially important in the age of precision medicine. Purity estimates have been shown to be critical for correction of tumor sequencing results, and higher purity samples allow for more accurate interpretations from next-generation sequencing results. Molecular-based purity estimates using computational approaches require sequencing of tumors, which is both time-consuming and expensive. METHODS: Here we propose an approach, weakly-supervised purity (wsPurity), which can accurately quantify tumor purity within a digitally captured hematoxylin and eosin (H&E) stained histological slide, using several types of cancer from The Cancer Genome Atlas (TCGA) as a proof-of-concept. FINDINGS: Our model predicts cancer type with high accuracy on unseen cancer slides from TCGA and shows promising generalizability to unseen data from an external cohort (F1-score of 0.83 for prostate adenocarcinoma). In addition we compare performance of our model on tumor purity prediction with a comparable fully-supervised approach on our TCGA held-out cohort and show our model has improved performance, as well as generalizability to unseen frozen slides (0.1543 MAE on an independent test cohort). In addition to tumor purity prediction, our approach identified high resolution tumor regions within a slide, and can also be used to stratify tumors into high and low tumor purity, using different cancer-dependent thresholds. INTERPRETATION: Overall, we demonstrate our deep learning model's different capabilities to analyze tumor H&E sections. We show our model is generalizable to unseen H&E stained slides from data from TCGA as well as data processed at Weill Cornell Medicine. FUNDING: Starr Cancer Consortium Grant (SCC I15-0027) to Iman Hajirasouliha.

Prognostic indicators of survival in patients with small-cell lung cancer at a tertiary care center in Lebanon.

OBJECTIVE: Small-cell lung cancer is a very aggressive tumor associated with high invasiveness and ease of metastasis and therefore poor prognosis. In the literature, several demographical, clinical as well as pathological factors including age, stage, gender and smoking were cited as independent prognosticators of survival. MATERIAL AND METHODS: This is a retrospective cohort study that includes 222 patients diagnosed with small-cell lung cancer between 2010 and 2019. Clinical and demographic data were extracted from their medical records. The Kaplan-Meier and logistic regression models of statistical analysis were used to evaluate the association of these variables with survival. RESULTS: Forty-five percent of patients were found to be alive at the time of data collection. The median survival of patients with small-cell lung cancer was found to be 14 months. On univariate analysis, increasing age as well as stage (extensive disease) were found to be significantly associated with decreased survival at 3 years. On the contrary, both gender and smoking status at diagnosis were not shown to significantly influence survival. On multivariate analysis, both age as well as stage remained significantly associated with survival. CONCLUSION: Limited data exist in the literature regarding the prognostic indicators of survival in small-cell lung cancer, especially from the Middle East area. In our study, both age and stage at the time of diagnosis were found to significantly influence survival. Further studies are needed to assess the association of other factors with survival.