Circulating tumor DNA (ctDNA) is an approved noninvasive biomarker to test for the presence of EGFR mutations at diagnosis or recurrence of lung cancer. However, studies evaluating ctDNA as a ...noninvasive "real-time" biomarker to provide prognostic and predictive information in treatment monitoring have given inconsistent results, mainly due to methodological differences. We have recently validated a next-generation sequencing (NGS) approach to detect ctDNA. Using this new approach, we evaluated the clinical usefulness of ctDNA monitoring in a prospective observational series of patients with non-small cell lung cancer (NSCLC).
We recruited 124 patients with newly diagnosed advanced NSCLC for ctDNA monitoring. The primary objective was to analyze the prognostic value of baseline ctDNA on overall survival. ctDNA was assessed by ultra-deep targeted NGS using our dedicated variant caller algorithm. Common mutations were validated by digital PCR. Out of the 109 patients with at least one follow-up marker mutation, plasma samples were contributive at baseline (n = 105), at first evaluation (n = 85), and at tumor progression (n = 66). We found that the presence of ctDNA at baseline was an independent marker of poor prognosis, with a median overall survival of 13.6 versus 21.5 mo (adjusted hazard ratio HR 1.82, 95% CI 1.01-3.55, p = 0.045) and a median progression-free survival of 4.9 versus 10.4 mo (adjusted HR 2.14, 95% CI 1.30-3.67, p = 0.002). It was also related to the presence of bone and liver metastasis. At first evaluation (E1) after treatment initiation, residual ctDNA was an early predictor of treatment benefit as judged by best radiological response and progression-free survival. Finally, negative ctDNA at E1 was associated with overall survival independently of Response Evaluation Criteria in Solid Tumors (RECIST) (HR 3.27, 95% CI 1.66-6.40, p < 0.001). Study population heterogeneity, over-representation of EGFR-mutated patients, and heterogeneous treatment types might limit the conclusions of this study, which require future validation in independent populations.
In this study of patients with newly diagnosed NSCLC, we found that ctDNA detection using targeted NGS was associated with poor prognosis. The heterogeneity of lung cancer molecular alterations, particularly at time of progression, impairs the ability of individual gene testing to accurately detect ctDNA in unselected patients. Further investigations are needed to evaluate the clinical impact of earlier evaluation times at 1 or 2 wk. Supporting clinical decisions, such as early treatment switching based on ctDNA positivity at first evaluation, will require dedicated interventional studies.
In cancer research, the accuracy of the technology used for biomarkers detection is remarkably important. In this context, digital PCR represents a highly sensitive and reproducible method that could ...serve as an appropriate tool for tumor mutational status analysis. In particular, droplet-based digital PCR approaches have been developed for detection of tumor-specific mutated alleles within plasmatic circulating DNA. Such an approach calls for the development and validation of a very significant quantity of assays, which can be extremely costly and time consuming. Herein, we evaluated assays for the detection and quantification of various mutations occurring in three genes often misregulated in cancers: the epidermal growth factor receptor (EGFR), the v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) and the Tumoral Protein p53 (TP53) genes. In particular, commercial competitive allele-specific TaqMan® PCR (castPCR™) technology, as well as TaqMan® and ZEN™ assays, have been evaluated for EGFR p.L858R, p.T790M, p.L861Q point mutations and in-frame deletions Del19. Specificity and sensitivity have been determined on cell lines DNA, plasmatic circulating DNA of lung cancer patients or Horizon Diagnostics Reference Standards. To show the multiplexing capabilities of this technology, several multiplex panels for EGFR (several three- and four-plexes) have been developed, offering new "ready-to-use" tests for lung cancer patients.
Despite major advances, non-small cell lung cancer (NSCLC) remains the major cause of cancer-related death in developed countries. Metastasis and drug resistance are the main factors contributing to ...relapse and death. Epithelial-to-mesenchymal transition (EMT) is a complex molecular and cellular process involved in tissue remodelling that was extensively studied as an actor of tumour progression, metastasis and drug resistance in many cancer types and in lung cancers. Here we described with an emphasis on NSCLC how the changes in signalling pathways, transcription factors expression or microRNAs that occur in cancer promote EMT. Understanding the biology of EMT will help to define reversing process and treatment strategies. We will see that this complex mechanism is related to inflammation, cell mobility and stem cell features and that it is a dynamic process. The existence of intermediate phenotypes and tumour heterogeneity may be debated in the literature concerning EMT markers, EMT signatures and clinical consequences in NSCLC. However, given the role of EMT in metastasis and in drug resistance the development of EMT inhibitors is an interesting approach to counteract tumour progression and drug resistance. This review describes EMT involvement in cancer with an emphasis on NSCLC and microRNA regulation.
Detecting single-nucleotide variations and insertions/deletions in circulating tumor DNA is challenging because of their low allele frequency. The clinical use of circulating tumor DNA to ...characterize tumor genetic alterations requires new methods based on next-generation sequencing.
We developed a method based on quantification of error rate of each base position position error rate (PER). To identify mutations, a binomial test was used to compare the minor-allele frequency to the measured PER at each base position. This process was validated in control samples and in 373 plasma samples from patients with lung or pancreatic cancer.
Minimal mutated allele frequencies were 0.003 for single-nucleotide variations and 0.001 for insertions/deletions. Independent testing performed by droplet digital PCR (n = 231 plasma samples) showed strong agreement with the base-PER method (κ = 0.90).
Targeted next-generation sequencing analyzed with the base-PER method represents a robust and low cost method to detect circulating tumor DNA in patients with cancer.
Renal medullary carcinoma (RMC) and collecting duct carcinoma (CDC) are rare entities with a poor outcome. First-line metastatic treatment is based on gemcitabine + platinum chemotherapy (GC) regimen ...but retrospective data suggest enhanced anti-tumour activity with the addition of bevacizumab. Therefore, we performed a prospective assessment of the safety and efficacy of GC + bevacizumab in metastatic RMC/CDC.
We conducted a phase 2 open-label trial in 18 centres in France in patients with metastatic RMC/CDC and no prior systemic treatment. Patients received bevacizumab plus GC up to 6 cycles followed, for non-progressive disease, by maintenance therapy with bevacizumab until progression or unacceptable toxicity. The co-primary end-points were objective response rates (ORRs) and progression-free survival (PFS) at 6 months (ORR-6; PFS-6). PFS, overall survival (OS) and safety were secondary end-points. At interim analysis, the trial was closed due to toxicity and lack of efficacy.
From 2015 to 2019, 34 of the 41 planned patients have been enroled. After a median follow-up of 25 months, ORR-6 and PFS-6 were 29.4% and 47.1%, respectively. Median OS was 11.1 months (95% confidence interval CI: 7.6–24.2). Seven patients (20.6%) discontinued bevacizumab because of toxicities (hypertension, proteinuria, colonic perforation). Grade 3–4 toxicities were reported in 82% patients, the most common being haematologic toxicities and hypertension. Two patients experienced grade 5 toxicity (subdural haematoma related to bevacizumab and encephalopathy of unknown origin).
Our study showed no benefit for bevacizumab added to chemotherapy in metastatic RMC and CDC with higher than expected toxicity. Consequently, GC regimen remains a therapeutic option for RMC/CDC patients.
•The addition of bevacizumab to chemotherapy failed to increase efficacy.•The incidence of adverse events with bevacizumab was higher than expected.•Platinum plus gemcitabine remains an treatment option for metastatic CDC/RMC.
Tumor-infiltrating immune cells affect lung cancer outcome. However, the factors that influence the composition and function of the tumor immune environment remain poorly defined and need ...investigation, particularly in the era of immunotherapy.
To determine whether the tumoral immune environment is related to lung adenocarcinoma mutations.
This retrospective cohort included 316 consecutive patients with lung adenocarcinoma (225 men; 258 smokers) studied from 2001 to 2005 in a single center. We investigated the association of densities of intratumoral mature dendritic cells (mDCs), CD8
T cells, neutrophils, and macrophages with clinical and pathological variables and tumor cell mutation profiles obtained by next-generation sequencing.
In 282 tumors, we found 460 mutations, mainly in TP53 (59%), KRAS (40%), STK11 (24%), and EGFR (14%). Intratumoral CD8
T-cell density was high in smokers (P = 0.02) and TP53-mutated tumors (P = 0.02) and low in BRAF-mutated tumors (P = 0.005). Intratumoral mDC density was high with low pathological tumor stage (P = 0.01) and low with STK11 mutation (P = 0.004). Intratumoral neutrophil density was high and low with BRAF mutation (P = 0.04) and EGFR mutation (P = 0.02), respectively. Intratumoral macrophage density was low with EGFR mutation (P = 0.01). Intratumoral CD8
T-cell and mDC densities remained strong independent markers of overall survival (P = 0.001 and P = 0.02, respectively).
Intratumoral immune cell densities (mDCs, CD8
T cells, neutrophils, macrophages) were significantly associated with molecular alterations in adenocarcinoma underlying the interactions between cancer cells and their microenvironment.
STK11 is commonly mutated in lung cancer. In light of recent experimental data showing that specific STK11 mutants could acquire oncogenic activities due to the synthesis of a short STK11 isoform, we ...investigated whether this new classification of STK11 mutants could help refine its role as a prognostic marker. We conducted a retrospective high-throughput genotyping study in 567 resected non-squamous non-small-cell lung cancer (NSCLC) patients. STK11 exons 1 or 2 mutations (STK11ex1-2) with potential oncogenic activity were analyzed separately from exons 3 to 9 (STK11ex3-9). STK11ex1-2 and STK11ex3-9 mutations occurred in 5% and 14% of NSCLC. STK11 mutated patients were younger (P = .01) and smokers (P< .0001). STK11 mutations were significantly associated with KRAS and inversely with EGFR mutations. After a median follow-up of 7.2 years (95%CI 6.8-.4), patients with STK11ex1-2 mutation had a median OS of 24 months (95%CI 15-57) as compared to 69 months (95%CI 56-93) for wild-type (log-rank, P = .005) and to 91 months (95%CI 57-unreached) for STK11ex3-9 mutations (P = .003). In multivariate analysis, STK11ex1-2 mutations remained associated with a poor prognosis (P = .002). Results were validated in two public datasets. Western blots showed that STK11ex1-2 mutatedtumors expressed short STK11 isoforms. Finally using mRNAseq data from the TCGA cohort, we showed that a stroma-derived poor prognosis signature was enriched in STK11ex1-2 mutated tumors. All together our results show that STK11ex1-2 mutations delineate an aggressive subtype of lung cancer for which a targeted treatment through STK11 inhibition might offer new opportunities.
Multi-state processes (Webster, 2019) are commonly used to model the complex clinical evolution of diseases where patients progress through different states. In recent years, machine learning and ...deep learning algorithms have been proposed to improve the accuracy of these models' predictions (Wang et al., 2019). However, acceptability by patients and clinicians, as well as for regulatory compliance, require interpretability of these algorithms's predictions. Existing methods, such as the Permutation Feature Importance algorithm, have been adapted for interpreting predictions in black-box models for 2-state processes (corresponding to survival analysis). For generalizing these methods to multi-state models, we introduce a novel model-agnostic interpretability algorithm called Multi-State Counterfactual Perturbation Feature Importance (MS-CPFI) that computes feature importance scores for each transition of a general multi-state model, including survival, competing-risks, and illness-death models. MS-CPFI uses a new counterfactual perturbation method that allows interpreting feature effects while capturing the non-linear effects and potentially capturing time-dependent effects. Experimental results on simulations show that MS-CPFI increases model interpretability in the case of non-linear effects. Additionally, results on a real-world dataset for patients with breast cancer confirm that MS-CPFI can detect clinically important features and provide information on the disease progression by displaying features that are protective factors versus features that are risk factors for each stage of the disease. Overall, MS-CPFI is a promising model-agnostic interpretability algorithm for multi-state models, which can improve the interpretability of machine learning and deep learning algorithms in healthcare.
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