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Purpose: Preoperative (neoadjuvant) chemoradiotherapy (CRT) and total mesorectal excision is the standard treatment for rectal cancer patients (UICC stage II/III). Up to one-third of patients treated with CRT achieve a pathological complete response (pCR). These patients could be spared from surgery and its associated morbidity and mortality, and assigned to a “watch and wait” strategy. However, reliably identifying pCR based on clinical or imaging parameters remains challenging.
Experimental design: We generated gene-expression profiles of 175 patients with locally advanced rectal cancer enrolled in the CAO/ARO/AIO-94 and -04 trials. One hundred and sixty-one samples were used for building, training and validating a predictor of pCR using a machine learning algorithm. The performance of the classifier was validated in three independent cohorts, comprising 76 patients from (i) the CAO/ARO/AIO-94 and -04 trials (n = 14), (ii) a publicly available dataset (n = 38) and (iii) in 24 prospectively collected samples from the TransValid A trial.
Results: A 21-transcript signature yielded the best classification of pCR in 161 patients (Sensitivity: 0.31; AUC: 0.81), when not allowing misclassification of non-complete-responders (False-positive rate = 0). The classifier remained robust when applied to three independent datasets (n = 76).
Conclusion: The classifier can identify >1/3 of rectal cancer patients with a pCR while never classifying patients with an incomplete response as having pCR. Importantly, we could validate this finding in three independent datasets, including a prospectively collected cohort. Therefore, this classifier could help select rectal cancer patients for a “watch and wait” strategy.
Translational relevance: Forgoing surgery with its associated side effects could be an option for rectal cancer patients if the prediction of a pathological complete response (pCR) after preoperative chemoradiotherapy would be possible. Based on gene-expression profiles of 161 patients a classifier was developed and validated in three independent datasets (n = 76), identifying over 1/3 of patients with pCR, while never misclassifying a non-complete-responder. Therefore, the classifier can identify patients suited for “watch and wait”.
Comparative proteomics reveals a diagnostic signature for pulmonary head‐and‐neck cancer metastasis
(2018)
Patients with head‐and‐neck cancer can develop both lung metastasis and primary lung cancer during the course of their disease. Despite the clinical importance of discrimination, reliable diagnostic biomarkers are still lacking. Here, we have characterised a cohort of squamous cell lung (SQCLC) and head‐and‐neck (HNSCC) carcinomas by quantitative proteomics. In a training cohort, we quantified 4,957 proteins in 44 SQCLC and 30 HNSCC tumours. A total of 518 proteins were found to be differentially expressed between SQCLC and HNSCC, and some of these were identified as genetic dependencies in either of the two tumour types. Using supervised machine learning, we inferred a proteomic signature for the classification of squamous cell carcinomas as either SQCLC or HNSCC, with diagnostic accuracies of 90.5% and 86.8% in cross‐ and independent validations, respectively. Furthermore, application of this signature to a cohort of pulmonary squamous cell carcinomas of unknown origin leads to a significant prognostic separation. This study not only provides a diagnostic proteomic signature for classification of secondary lung tumours in HNSCC patients, but also represents a proteomic resource for HNSCC and SQCLC.
We have recently shown that caspase-8 is a new substrate of Polo-like kinase 3 (Plk3) that phosphorylates the protein on residue T273 thereby promoting its pro-apoptotic function. In the present study we aimed to investigate the clinical relevance of Plk3 expression and phosphorylation of caspase-8 at T273 in patients with anal squamous cell carcinoma (SSC) treated with 5-fluorouracil and mitomycin C-based chemoradiotherapy (CRT). Immunohistochemical detection of the markers was performed in pretreatment biopsy specimens of 95 patients and was correlated with clinical/histopathologic characteristics including HPV-16 virus load/p16INK4a expression and cumulative incidence of local and distant failure, cancer specific survival (CSS), and overall survival (OS). We observed significant positive correlations between Plk3 expression, pT273 caspase-8 signal, and levels of HPV-16 virus DNA load/p16INK4a detection. Patients with high scores of Plk3 and pT273 caspase-8 showed increased local control (p = 0.011; p = 0.001), increased CSS (p = 0.011; p = 0.013) and OS (p = 0.024; p = 0.001), while the levels of pT273 caspase-8 were significantly associated (p = 0.033) with distant metastases. In multivariate analyses Plk3 expression remained significant for local failure (p = 0.018), CSS (p = 0.016) and OS (p = 0.023). Moreover, a combined HPV16 DNA load and Plk3 or pT273 caspase-8 variable revealed a significant correlation to decreased local failure (p = 0.001; p = 0.009), increased CSS (p = 0.016; p = 0.023) and OS (p = 0.003; p = 0.003). In conclusion these data indicate that elevated levels of Plk3 and pT273 caspase-8 are correlated with favorable clinical outcome in patients with anal SCC treated with concomitant CRT.