Innovative Tumor Tissue Dissection Tool for Molecular Oncology Diagnostics

Precision needle-punch tumor enrichment from paraffin blocks improves the detection of clinically actionable genomic alterations and biomarkers

Background

While many molecular assays can detect mutations at low tumor purity and variant allele frequencies, complex biomarkers such as tumor mutational burden (TMB), microsatellite instability (MSI), and genomic loss of heterozygosity (gLOH) require higher tumor purity for accurate measurement. Scalable, quality-controlled, tissue-conserving methods to increase tumor nuclei percentage (TN%) from tumor specimens are needed for complex biomarkers and hence necessary to maximize patient matching to approved therapies or clinical trial enrollment. We evaluated the clinical utility and performance of precision needle-punch enrichment (NPE) compared with traditional razor blade macroenrichment of tumor specimens on molecular testing success.

Methods

Pathologist-directed NPE was performed manually on formalin-fixed, paraffin embedded (FFPE) blocks. Quality control of target capture region and quantity of residual tumor in each tissue block was determined via a post-enrichment histologic slide recut. Resultant tumor purity and biomarker status were determined by the computational analysis pipeline component of the FDA-approved next-generation sequencing (NGS) assay, FoundationOne®CDx. Following NPE implementation for real-world clinical samples, assay performance and biomarker (MSI, TMB, gLOH) detection were analyzed.

Results

In real-world clinical samples, enrichment rate via NPE was increased to ~50% over a 2.5-year period, exceeding the prior use of razor blade macro-enrichment (<30% of cases) prior to NPE implementation due to proven efficacy in generating high quality molecular results from marginal samples and the ease of use for both pathologist and histotechnologists. NPE was associated with lower test failures, higher computational tumor purity, and higher rates of successful TMB, MSI and gLOH determination when stratified by pre-enriched (incipient) tumor nuclei percentage. In addition, challenging cases in which tumor content was initially insufficient for testing were salvaged for analysis of biomarker status, gene amplification/deletion, and confident mutant or wild-type gene status determination.

Conclusions

Pathologist-directed precision enrichment from tissue blocks (aka NPE) increases tumor purity, and consequently, yields a greater number of successful tests and complex biomarker determinations. Moreover, this process is rapid, safe, inexpensive, scalable, and conserves patient surgical pathology material. NPE may constitute best practice with respect to enriching tumor cells from low-purity specimens for biomarker detection in molecular laboratories.

Comparison of diagnostic methods for detection of BRAFV600E mutation in ameloblastoma

BACKGROUND

Ameloblastoma is an aggressively growing, highly recurrent odontogenic jaw tumor. Its association with BRAFV600E mutation is an indication for BRAFV00E-inhibitor therapy The study objective was to identify a sensitive low-cost test for BRAFV600E-positive ameloblastoma. We hypothesized that immunohistochemical staining of formalin-fixed paraffin-embedded tissues for BRAFV600E mutation is a low-cost surrogate for BRAFV600E gene sequencing when laboratory resources are inadequate for molecular testing.

METHODS

Tissues from 40 ameloblastoma samples were retrieved from either formalin-fixed paraffin-embedded blocks, RNAlater™ stabilization solution or samples inadvertently pre-fixed in formalin before transfer to RNAlater™. BRAFV600E mutation was assessed by Direct Sanger sequencing, Amplification Refractory Mutation System-PCR and immunohistochemistry (IHC).

RESULTS

BRAFV600E mutation was detected by IHC, Amplification Refractory Mutation System-PCR and Direct Sanger sequencing in 93.33%, 52.5% and 30% of samples respectively. Considering Direct Sanger sequencing as standard BRAFV600E detection method, there was significant difference between the three detection methods (𝜒2 (2) = 31.34, p < 0.0001). Sensitivity and specificity of IHC were 0.8 (95% CI: 0.64-0.90) and 0.9 (95% CI: 0.75-0.99) respectively, while positive predictive value and negative predictive value (NPV) were 0.9 and 0.8 (Fischer's test, p < 0.0001) respectively. Sensitivity and specificity of Amplification Refractory Mutation System-PCR detection method were 0.7 (95% CI: 0.53-0.80) and 0.9 (95% CI = 0.67-0.98) respectively, while PPV and NPV were 0.9 and 0.6 respectively (Fischer's test, p < 0.0001).

CONCLUSION

Low-cost and less vulnerability of IHC to tissue quality make it a viable surrogate test for BRAFV600E detection in ameloblastoma. Sequential dual IHC and molecular testing for BRAFV600E will reduce equivocal results that could exclude some patients from BRAFV600E-inhibitor therapies.

A Critical Issue in Lung Cancer Cytology and Small Biopsies: DNA and RNA Extraction from Archival Stained Slides for Biomarker Detection through Real Time PCR and NGS-The Experience in Pathological Anatomy Unit

The handling of biomaterials is crucial for precision medicine in advanced-stage lung patients with only cytology or small biopsies available. The main purpose of the study was to evaluate the quantity and quality of nucleic acids extracted from mixed stained slides (MSSs), including H&E, IHC and FISH, compared to the extraction from unstained slides (USs). A series of 35 lung adenocarcinoma surgical samples was selected to set up the method and the technical approach was validated in a series of 15 small biopsies and 38 cytological samples. DNA extracted from MSSs was adequate in all samples and the Real Time PCR was successful in 30/35 surgical samples (86%), 14/15 small biopsies (93%), and 33/38 cytological samples (87%). NGS using DNA extracted from MSSs was successful in 18/35 surgical samples (51%), 11/15 small biopsies (73%), and 26/38 cytological samples (68%). RNA extracted from MSSs was unsatisfactory in all cases showing an inadequate degree of fragmentation. Our technical approach based on the recovery of stained slides could represent a strategic way forward for DNA-based biomarker testing in lung cancer cases without biomaterials. The RNA extracted from MSSs did not represent a successful approach.

Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides

To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Unfortunately, misestimation may cause tissue waste and increased laboratory costs. We developed an artificial intelligence (AI)-augmented smart pathology review system (SmartPath) to empower pathologists with quantitative metrics for accurately determining tissue extraction parameters. SmartPath uses two deep learning architectures, a U-Net based network for cell segmentation and a multi-field-of-view convolutional network for tumor area segmentation, to extract features from digitized H&E-stained formalin-fixed paraffin-embedded slides. From the segmented tumor area, SmartPath suggests a macrodissection area. To predict DNA yield per slide, the extracted features from within the macrodissection area are correlated with known DNA yields to fit a regularized linear model (R = 0.85). Then, a pathologist-defined target yield divided by the predicted DNA yield per slide gives the number of slides to scrape. Following model development, an internal validation trial was conducted within the Tempus Labs molecular sequencing laboratory. We evaluated our system on 501 clinical colorectal cancer slides, where half received SmartPath-augmented review and half traditional pathologist review. The SmartPath cohort had 25% more DNA yields within a desired target range of 100-2000 ng. The number of extraction attempts was statistically unchanged between cohorts. The SmartPath system recommended fewer slides to scrape for large tissue sections, saving tissue in these cases. Conversely, SmartPath recommended more slides to scrape for samples with scant tissue sections, especially those with degraded DNA, helping prevent costly re-extraction due to insufficient extraction yield. A statistical analysis was performed to measure the impact of covariates on the results, offering insights on how to improve future applications of SmartPath. With these improvements, AI-augmented histopathologic review has the potential to decrease tissue waste, sequencing time, and laboratory costs by optimizing DNA yields, especially for samples with scant tissue and/or degraded DNA.