Tumour infiltrating lymphocytes and survival after adjuvant chemotherapy in patients with gastric cancer: post-hoc analysis of the CLASSIC trial

BACKGROUND

Only a subset of gastric cancer (GC) patients with stage II-III benefits from chemotherapy after surgery. Tumour infiltrating lymphocytes per area (TIL density) has been suggested as a potential predictive biomarker of chemotherapy benefit.

METHODS

We quantified TIL density in digital images of haematoxylin-eosin (HE) stained tissue using deep learning in 307 GC patients of the Yonsei Cancer Center (YCC) (193 surgery+adjuvant chemotherapy [S + C], 114 surgery alone [S]) and 629 CLASSIC trial GC patients (325 S + C and 304 S). The relationship between TIL density, disease-free survival (DFS) and clinicopathological variables was analysed.

RESULTS

YCC S patients and CLASSIC S patients with high TIL density had longer DFS than S patients with low TIL density (P = 0.007 and P = 0.013, respectively). Furthermore, CLASSIC patients with low TIL density had longer DFS if treated with S + C compared to S (P = 0.003). No significant relationship of TIL density with other clinicopathological variables was found.

CONCLUSION

This is the first study to suggest TIL density automatically quantified in routine HE stained tissue sections as a novel, clinically useful biomarker to identify stage II-III GC patients deriving benefit from adjuvant chemotherapy. Validation of our results in a prospective study is warranted.

Immunotherapies for hepatocellular carcinoma

Cases of hepatocellular carcinoma (HCC) are rapidly rising. This is particularly the case in the Western world, as a result of increasing rates of chronic liver disease, secondary to lifestyle-associated risk factors and the lack of an established screening programme for the general population. Traditionally, radical/curative treatment options for HCC, including liver transplantation and surgical resection are reserved for the minority of patients, presenting with an early stage cancer. For patients with advanced disease, Sorafenib and Lenvatinib were, until recently, the only licensed systemic treatments, and provided only limited survival benefits at the cost of a multitude of potential side effects. Recent scientific advances in the field of cancer immunotherapy have renewed significant interest in advanced HCC, in order to fulfil this apparent area of unmet clinical need. This has led to the success and recent regulatory approval of an Atezolizumab/Bevacizumab combination for the first-line treatment of advanced HCC following results from the IMbrave150 clinical trial in 2019, with further immune checkpoint inhibitors currently undergoing testing in advanced clinical trials. Furthermore, other cancer immunotherapies, including chimeric antigen receptor T-cells, dendritic cell vaccines and oncolytic viruses are also in early stage clinical trials, for the treatment of advanced HCC. This review will summarise the major approaches that have been and are currently in development for the systemic treatment of advanced HCC, their advantages, drawbacks, and predictions of where this revolutionary treatment field will continue to travel for the foreseeable future.

Characterising cancer-associated fibroblast heterogeneity in non-small cell lung cancer: a systematic review and meta-analysis

Cancer-associated fibroblasts (CAFs) are a key component of the tumour microenvironment with evidence suggesting they represent a heterogeneous population. This study summarises the prognostic role of all proteins characterised in CAFs with immunohistochemistry in non-small cell lung cancer thus far. The functions of these proteins in cellular processes crucial to CAFs are also analysed. Five databases were searched to extract survival outcomes from published studies and statistical techniques, including a novel method, used to capture missing values from the literature. A total of 26 proteins were identified, 21 of which were combined into 7 common cellular processes key to CAFs. Quality assessments for sensitivity analyses were carried out for each study using the REMARK criteria whilst publication bias was assessed using funnel plots. Random effects models consistently identified the expression of podoplanin (Overall Survival (OS)/Disease-specific Survival (DSS), univariate analysis HR 2.25, 95% CIs 1.80-2.82) and α-SMA (OS/DSS, univariate analysis HR 2.11, 95% CIs 1.18-3.77) in CAFs as highly prognostic regardless of outcome measure or analysis method. Moreover, proteins involved in maintaining and generating the CAF phenotype (α-SMA, TGF-β and p-Smad2) proved highly significant after sensitivity analysis (HR 2.74, 95% CIs 1.74-4.33) supporting attempts at targeting this pathway for therapeutic benefit.

A non-linear optimisation method to extract summary statistics from Kaplan-Meier survival plots using the published P value

Background

Meta-analyses of studies evaluating survival (time-to-event) outcomes are a powerful technique to assess the strength of evidence for a given disease or treatment. However, these studies rely on the adequate reporting of summary statistics in the source articles to facilitate further analysis. Unfortunately, many studies, especially within the field of prognostic research do not report such statistics, making secondary analyses challenging. Consequently, methods have been developed to infer missing statistics from the commonly published Kaplan-Meier (KM) plots but are liable to error especially when the published number at risk is not included.

Methods

We therefore developed a method using non-linear optimisation (nlopt) that only requires the KM plot and the commonly published P value to better estimate the underlying censoring pattern. We use this information to then calculate the natural logarithm of the hazard ratio (ln (HR)) and its variance (var) ln (HR), statistics important for meta-analyses.

Results

We compared this method to the Parmar method which also does not require the number at risk to be published. In a validation set consisting of 13 KM studies, a statistically significant improvement in calculating ln (HR) when using an exact P value was obtained (mean absolute error 0.014 vs 0.077, P = 0.003). Thus, when the true HR has a value of 1.5, inference of the HR using the proposed method would set limits between 1.49/1.52, an improvement of the 1.39/1.62 limits obtained using the Parmar method. We also used Monte Carlo simulations to establish recommendations for the number and positioning of points required for the method.

Conclusion

The proposed non-linear optimisation method is an improvement on the existing method when only a KM plot and P value are included and as such will enhance the accuracy of meta-analyses performed for studies analysing time-to-event outcomes. The nlopt source code is available, as is a simple-to-use web implementation of the method.

Asymmetric mode of Ca²⁺-S100A4 interaction with nonmuscle myosin IIA generates nanomolar affinity required for filament remodeling

Filament assembly of nonmuscle myosin IIA (NMIIA) is selectively regulated by the small Ca²⁺-binding protein, S100A4, which causes enhanced cell migration and metastasis in certain cancers. Our NMR structure shows that an S100A4 dimer binds to a single myosin heavy chain in an asymmetrical configuration. NMIIA in the complex forms a continuous helix that stretches across the surface of S100A4 and engages the Ca²⁺-dependent binding sites of each subunit in the dimer. Synergy between these sites leads to a very tight association (K_D ∼1 nM) that is unique in the S100 family. Single-residue mutations that remove this synergy weaken binding and ameliorate the effects of S100A4 on NMIIA filament assembly and cell spreading in A431 human epithelial carcinoma cells. We propose a model for NMIIA filament disassembly by S100A4 in which initial binding to the unstructured NMIIA tail initiates unzipping of the coiled coil and disruption of filament packing.