An online randomised controlled trial of prognosticating imminent death in advanced cancer patients: Clinicians give greater weight to advice from a prognostic algorithm than from another clinician with a different profession

BACKGROUND

A second opinion or a prognostic algorithm may increase prognostic accuracy. This study assessed the level to which clinicians integrate advice perceived to be coming from another clinician or a prognostic algorithm into their prognostic estimates, and how participant characteristics and nature of advice received affect this.

METHODS

An online double-blind randomised controlled trial was conducted. Palliative doctors, nurses and other types of healthcare professionals were randomised into study arms differing by perceived source of advice (algorithm or another clinician). In fact, the advice was the same in both arms (emanating from the PiPS-B14 prognostic model). Each participant reviewed five patient summaries. For each summary, participants: (1) provided an initial probability estimate of two-week survival (0% 'certain death'-100% 'certain survival'); (2) received advice (another estimate); (3) provided a final estimate. Weight of Advice (WOA) was calculated for each summary (0 '100% advice discounting' - 1 '0% discounting') and multilevel linear regression analyses were conducted.

CLINICAL TRIAL REGISTRATION NUMBER

NCT04568629.

RESULTS

A total of 283 clinicians were included in the analysis. Clinicians integrated advice from the algorithm more than advice from another clinician (WOA difference = -0.12 [95% CI -0.18, -0.07], p < 0.001). There was no interaction between study arm and participant profession, years of palliative care or overall experience. Advice of intermediate strength (75%) was given a lower WOA (0.31) than advice received at either the 50% (WOA 0.40) or 90% level (WOA 0.43). The overall interaction between strength of advice and study arm on WOA was significant (p < 0.001).

CONCLUSION

Clinicians adjusted their prognostic estimates more when advice was perceived to come from a prognostic algorithm than from another clinician. Research is needed to understand how clinicians make prognostic decisions and how algorithms are used in clinical practice.

A Dermatologist's Guide to Implementation of Gene Expression Profiling in the Management of Melanoma

BACKGROUND. With the advent of effective therapeutics, melanoma mortality rates have decreased, yet incidence rates are continuing to rise, making accurate prognostication for risk of recurrence increasingly important. Gene expression profiling (GEP) is a clinically available, objective metric that can be used in conjunction with traditional clinicopathological staging to help physicians stratify risk in melanoma patients. There is a gap in guidance from the American Joint Committee on Cancer (AJCC) and the National Comprehensive Cancer Network (NCCN) regarding how to utilize GEP in melanoma care. OBJECTIVE. An expert panel of 31-GEP test users sought to provide clarification of use options and a rational clinical workflow to guide appropriate application of the 31- GEP test in everyday practice. METHODS. The authors participated in an in-depth review of the literature and panel discussion regarding current limitations of melanoma risk assessment and opportunities for improvement with GEP. The panel reviewed 1) validation and clinical impact data supporting the use of sentinel lymph node biopsy (SLNB), 2) existing primary data and meta-analyses for 31-GEP testing in melanoma risk assessment, 3) AJCC, NCCN, and Melanoma Prevention Working Group (MPWG) data and guidelines for GEP use in melanoma risk assessment, and 4) experiences, rationales, and scenarios in which 31-GEP testing may be helpful for risk assessment. RESULTS. The 31-GEP test is useful and actionable for patient care when applied in accordance with current NCCN guidelines. Stratification of patients into low (Class 1a), intermediate (Class 1b or 2a), or high (Class 2b) risk categories can inform multidisciplinary conference discussion and can assist with determining the intensity of imaging, surveillance, and follow-up care. Patient-specific features of the disease and individual circumstances should be considered in the decision to use 31-GEP testing. CONCLUSION. The authors suggest a clinical workflow that integrates 31-GEP testing under the umbrella of current national guidelines. Application of the test in appropriate patient populations can improve risk assessment and inform clinical decision-making.

Kallikreins Stepwise Scoring Reveals Three Subtypes of Papillary Thyroid Cancer with Prognostic Implications

BACKGROUND

Papillary thyroid cancer (PTC) is the most common type of thyroid cancer. Unlike most cancers, its incidence has dramatically increased in the last decades mainly due to increased diagnosis of indolent PTCs. Adequate risk stratification is crucial to avoid the over-treatment of low-risk patients, as well as the under-treatment of high-risk patients, but the currently available markers are still insufficient. Kallikreins (KLKs) are emergent biomarkers in cancer, but their involvement in PTC is unknown.

METHODS

This study analyzed DNA methylation (HumanMethylation arrays) and gene expression (RNA-Seq) of KLKs, BRAF and RAS mutations, and clinical data from four published thyroid cancer data sets including normal and tumor tissues (n = 73, n = 475, n = 20, and n = 82) as discovery, training, and validation series. The C4.5 classification algorithm was used to generate a decision tree. Disease-free survival was estimated using Kaplan-Meier and Cox approaches. Specific analyses were performed using real-time polymerase chain reaction and immunohistochemistry.

RESULTS

The entire KLK family was deregulated in PTC, displaying a specific epigenetic and transcriptional profile strongly associated with BRAF^V600E or RAS mutations. Thus, a decision-tree algorithm was developed based on three KLKs with >80% sensitivity and >95% specificity, identifying BRAF- and RAS-mutated tumors. Notably, tumors lacking these mutations were classified as BRAF- or RAS-like. Most importantly, the KLK algorithm uncovered a novel PTC subtype showing favorable prognostic features.

CONCLUSIONS

The KLK algorithm could lead to a new clinically applicable strategy with important implications for the risk stratification of PTC and the management of patients.

Mutational analysis of merkel cell carcinoma

Merkel cell carcinoma (MCC) is an aggressive cutaneous neuroendocrine malignancy that is associated with a poor prognosis. The pathogenesis of MCC is not well understood, and despite a recent plethora of mutational analyses, we have yet to find a set of signature mutations implicated in the majority of cases. Mutations, including TP53, Retinoblastoma and PIK3CA, have been documented in subsets of patients. Other mechanisms are also likely at play, including infection with the Merkel cell polyomavirus in a subset of patients, dysregulated immune surveillance, epigenetic alterations, aberrant protein expression, posttranslational modifications and microRNAs. In this review, we summarize what is known about MCC genetic mutations and chromosomal abnormalities, and their clinical significance. We also examine aberrant protein function and microRNA expression, and discuss the therapeutic and prognostic implications of these findings. Multiple clinical trials designed to selectively target overexpressed oncogenes in MCC are currently underway, though most are still in early phases. As we accumulate more molecular data on MCC, we will be better able to understand its pathogenic mechanisms, develop libraries of targeted therapies, and define molecular prognostic signatures to enhance our clinicopathologic knowledge.