Diagnostics and Biosensing

Assessment of Prostate Cancer Progression and Metastasis and Patient Stratification

Prostate cancer (PCa) is one of the leading causes of death among male cancer patients. Although often being characterized by slow progression, in some patients it is very aggressive and moves fast from the prostate to the lymph nodes and other distant secondary sites, such as bone. PCa is also characterized as a very heterogenous tissue, which makes accurate diagnosis extremely complex. Current diagnostic practices for prostate cancer assessment include clinical staging, prostate specific antigen (PSA) quantification, and Gleason grading of biopsied tissues. Although these concurrent approaches have been validated and approved, they are still controversial. For instance, PSA levels can increase even in the absence of cancer, as a consequence of other diseases of the prostate, and can be high even after therapy (e.g., androgen deprivation therapy, ADT) as the result of what is known as biochemical recurrence. Therefore, PSA testing can lead to overtreatment. Additionally, Gleason grading, although providing improved matching to clinical outcomes after being revisited in 2014, can still lead to erroneous diagnoses because of the heterogeneity of the tissues and inter-observer irreproducibility.

The 2014 revised Gleason grading system assigns a Gleason score to biopsied tissues collected from different sites of the prostate, depending on histological tumor morphology variants, with higher scores assigned to more undifferentiated tissues, i.e., tissues that progressively look less and less like healthy tissues. To make up for the heterogeneity of PCa tissues, the method assigns two scores (e.g., 3 and 4) where the first score is indicative of the structure of the majority tissue (in this case 3), whereas the second describes the minority, surrounding tissue (in this case 4). For this hypothetical sample, the score would then be 3 + 4 = 7. The grading system then compounds the collected scores into grade groups I to V, where I and II are groups for which therapy deferral (or watchful waiting) is recommended, whereas groups III and higher indicate a more advanced disease for which surgery and/or therapy (radiation, hormonal, or chemo) are recommended. The main issue with the Gleason grading system is that it often fails to discriminate among low-risk and high-risk tissues, making it necessary to identify new approaches for patient stratification.

One of the recent new approaches for improving diagnosis and patient stratification involves the discovery and use of new biomarkers other than PSA. Among these, the prostate specific membrane antigen (PSMA), a type II transmembrane protein that is specific to all forms of prostate tissue, has been identified as a therapeutically relevant biomarker and validated clinically.  Increased PSMA expression has been associated with higher recurrence of the tumor, which makes it an attractive target. Although recent literature has shown that PSMA levels accurately correlate with PCa aggressiveness and baseline PSA serum levels, we believe that immunohistochemistry-based assessment can only marginally provide the spatial resolution and sensitivity necessary to take into account the high tissue variability and the PSMA expression level in healthy tissues, calling for the need of a more sensitive and spatially resolved technique. Our results show that SERS outperforms fluorescence-based immunohistochemistry for quantification of PSMA and enables the stratification of patients in three clearly distinct recommended therapy groups, namely, the watchful waiting group (group 1), the nonmetastatic active therapy group (group 2), and the metastasized and/or castration resistant group (group 3), based on compounded PSMA expression data. This retrospective study allowed not only to confirm PSMA as an effective biomarker for the evaluation of disease stage but also led to an improved stratification of patients into groups of recommended therapeutic regimen. In the future, the implementation of the approach in longitudinal studies promises to become a valuable method for monitoring disease progression and response to therapy.

Relevant References.

Bhamidipati, M.; Lee, G.; Kim, I.; Fabris, L. SERS-based Quantification of PSMA in Tissue Microarrays Allows Effective Stratification of Prostate Cancer Patients. ACS Omega 2018, 3, 16784.

Bhamidipati, M.; Cho, H. Y.; Lee, K.-B.; Fabris, L. SERS-based Quantification of Biomarker Expression at the Single Cell Level Enabled by Gold Nanostars and Truncated Aptamers. Bioconj. Chem. 2018, 29, 2970.

Viral Evolution: Can we Design an Improved Vaccine for Influenza?

Viral infections are a leading cause of morbidity and mortality. Influenza A, a highly contagious recurring seasonal epidemic, is particular cause of concern due to the unpredictable effectiveness of its vaccine. According to the Center for Disease Control and Prevention, the effectiveness of the 2016-2017 vaccine against the influenza A virus (IAV) ranged between 57 % to as low as 13 %, depending on the age group. IAV is a highly mutating human RNA virus that grows resistant to drugs and vaccines as it replicates, due to its high mutation rates. Rapid evolution in RNA viruses is responsible not only for the low effectiveness of vaccines existing for viruses such as IAV, but also for the lack of vaccines for others (e.g. HIV). The impact on the healthcare systems worldwide and the repercussions on patients are substantial, and cause of concern, especially when taking into account the likelihood of another pandemic such as that caused in 2009 by the H1N1 subtype influenza virus. The ability of RNA viruses to adapt to a new host is linked to the rapid generation of de novo genomic diversity, with mutation rates affected by a series of factors, among which polymerase fidelity is one of the most important. Understanding viral mutations holds significant importance because of its wide impact on new vaccine design, drug resistance management, and prediction of new pathogenesis. It has been observed that certain mutations can be detected with frequencies higher than others and are often associated to the packaging segments within the viral genome, resulting in defective genes that carry less pathogenicity compared to the wild type. These defective genes are defined as defective interfering particles (DIPs). Currently, DIPs are the focus of a substantial research field in evolutionary virology with enormous potential toward a universal vaccine for IAV.

Studying RNA mutations and IAV evolution to predict and understand the mechanisms of viral mutations is therefore of extreme importance from both the preventative and therapeutic standpoints. In particular, understanding the sequence of events and the factors that contribute to IAV’s evolutionary success in drug resistance can pave the way for more effective treatments and preventative measures. Traditional RNA detection and quantification approaches, however, typically leveraging costly RNA sequencing tools, address these viral evolution questions at the population level. It has become increasingly apparent that important information may be lost with this bulk approach, as population outliers may hold the mechanistic key to understanding how the infection establishes itself in the host and how it can be mitigated or halted. Therefore, quantifying viral RNA mutations at the single cell level is critical to study viral evolution and ultimately to achieve viral population control.

Our group designs intracellular tools based on gold nanostars that can identify the virus in intact individual cells and quantify the presence of mutations in its RNA.