The plasma levels of the longer fragments (Alu 247) of cfDNA and the cfDNA integrity index are promising markers to predict tumor response after preoperative CRT for rectal cancer.
Epidemiological evidence supports the potential of vitamin B6 as a cancer risk reduction agent and the role of PLP as a cancer screening biomarker, especially for gastrointestinal tumors. However, inconsistent findings from total intake and intervention studies suggest that vitamin B6 might also be an indicator of other dietary protective micronutrients.
Background:Colorectal cancer (CRC) is an important cause of cancer-related death. Prediction of recurrence is an important issue in the treatment of disease, particularly for stage II patients. The level of telomere-specific reverse transcriptase (hTERT), the catalytic component of the telomerase complex, increases along with CRC progression, but its prognostic value is still unclear.Methods:One hundred and thirty-seven CRC patients were studied for hTERT expression in tumour cells by real-time PCR. hTERT level was evaluated as a prognostic factor of overall survival (OS) in all patients and of disease recurrence in a subgroup of 50 stage II patients.Results:The median hTERT level was 93.8 copies (interquartile range 48–254). Patients with high hTERT levels (above the median) showed a significantly worse survival than those with low hTERT levels (below the median; log-rank test P<0.0001; hazard ratio (HR)=3.30 (95% confidence interval (CI) 1.98–5.52); P<0.0001). The negative prognostic value of high hTERT level is independent of the pathological stage and microsatellite instability (HR=2.09 (95% CI 1.20–3.64), P=0.009). Moreover, in stage II CRC, high hTERT levels identified patients with a higher risk of disease recurrence (HR=3.06 (95% CI 1.03–9.04), P=0.043) and death (HR=3.24 (95% CI 1.37–7.71), P=0.008).Conclusion:hTERT level is an independent prognostic marker of OS in CRC patients. In addition, assessment of hTERT level could improve stratification of stage II CRC patients for the risk of disease recurrence.
CD133-positive CTC may represent a suitable prognostic marker to stratify the risk of patients who undergo liver resection for CRC metastasis, which opens the avenue to identifying and potentially monitoring the patients who are most likely to benefit from adjuvant treatments.
There is no reliable evidence to support the use of pancreatojejunostomy over pancreatogastrostomy. Future large international studies may shed new light on this field of investigation.
BackgroundThe efficacy of current anticancer treatments is far from satisfactory and many patients still die of their disease. A general agreement exists on the urgency of developing molecularly targeted therapies, although their implementation in the clinical setting is in its infancy. In fact, despite the wealth of preclinical studies addressing these issues, the difficulty of testing each targeted therapy hypothesis in the clinical arena represents an intrinsic obstacle. As a consequence, we are witnessing a paradoxical situation where most hypotheses about the molecular and cellular biology of cancer remain clinically untested and therefore do not translate into a therapeutic benefit for patients.ObjectiveTo present a computational method aimed to comprehensively exploit the scientific knowledge in order to foster the development of personalized cancer treatment by matching the patient's molecular profile with the available evidence on targeted therapy.MethodsTo this aim we focused on melanoma, an increasingly diagnosed malignancy for which the need for novel therapeutic approaches is paradigmatic since no effective treatment is available in the advanced setting. Relevant data were manually extracted from peer-reviewed full-text original articles describing any type of anti-melanoma targeted therapy tested in any type of experimental or clinical model. To this purpose, Medline, Embase, Cancerlit and the Cochrane databases were searched.Results and ConclusionsWe created a manually annotated database (Targeted Therapy Database, TTD) where the relevant data are gathered in a formal representation that can be computationally analyzed. Dedicated algorithms were set up for the identification of the prevalent therapeutic hypotheses based on the available evidence and for ranking treatments based on the molecular profile of individual patients. In this essay we describe the principles and computational algorithms of an original method developed to fully exploit the available knowledge on cancer biology with the ultimate goal of fruitfully driving both preclinical and clinical research on anticancer targeted therapy. In the light of its theoretical nature, the prediction performance of this model must be validated before it can be implemented in the clinical setting.
These results provide physicians and health care regulatory agencies with RCT-based evidence on efficacy and acceptability of currently available breast cancer CPAs; at the same time, we pinpoint how much work still remains to be done before pharmacological primary prevention becomes a routine option to reduce the burden of this disease.
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