BACKGROUND:
The treatment of human cancer has been seriously hampered for decades by resistance to chemotherapeutic drugs. A very efficient mechanism of tumor resistance to drugs is the proton pumps-mediated acidification of tumor microenvironment. Metronomic chemotherapy has shown efficacy in adjuvant fashion as well as in the treatment of pets with advanced disease. Moreover, we have shown in veterinary clinical settings that pre-treatment with proton-pumps inhibitors (PPI) increases tumor responsiveness to chemotherapeutics. In this study pet with spontaneously occurring cancer have been recruited to be treated by a combination of metronomic chemotherapy and high dose PPIs and their responses have been matched to those of a historical control of ten patients treated with metronomic chemotherapy alone.
METHODS:
Single arm, non randomized phase II open study, with historical control group, evaluating safety and efficacy of the combination of metronomic chemotherapy and alkalization. Twenty-four companion animals (22 dogs and 2 cats) were treated adding to their metronomic chemotherapy protocol the pump inhibitor lansoprazole at high dose, and a water alkalizer. Their responses have been evaluated by clinical and instrumental evaluation and matched to those of the control group.
RESULTS:
The protocol was overall well tolerated, with only two dogs experiencing side effects due to gastric hypochlorhydria consisting with vomiting and or diarrhea. In terms of overall response, in the alkalized cohort, 18 out of 24 had partial or complete responses (75%), two patients had a stable disease and the remaining patients experienced no response or progressive disease. On the other hand, only one patient in the control group experienced a complete response (10%) and three other experienced short lived responses. Median time to terminal event was 34 weeks for the experimental group versus 2 weeks in the controls (p= 0.042).
CONCLUSIONS:
Patient alkalization has shown to be well tolerated and to increase tumor response to metronomic chemotherapy as well the quality of life in pets with advanced cancer. Further studies are warranted to assess the efficacy of this strategy in patients with advanced cancers in companion animals as well as in humans.
Search results
Items: 4
1.
Kaplan-Meier survival curve for alkalized patients (red line) and controls (blue line).
2.
Histogram representation of the owners’ percentage and degree of satisfaction for the clinical outcome of their pets in the PPI and control groups.
3.
A canine patient with lung cancer treated with metronomic chemotherapy and alkalization at presentation (A) and at four months control (B).
4.
A canine patient with a nasal sarcoma at presentation (A) and after 4 months of therapy (B), the dog had a nasal sinus sarcoma that underwent PR resulting in cessation of nasal discharge and bleeding as well as pawing at the lesion. Another patient with a large ulcerated high grade mammary carcinoma (C) experiencing a long lasting PR (D).
Search results
Items: 6
1.
Survivorship curves from control and experimental treatments. Part (A) shows survivorship curves associated with control cohorts from the 18 comparisons listed in . Part (B) shows survivorship curves associated with (long-lived) experimental cohorts from the 22 comparisons listed in .
2.
Deceleration factors estimates and control cohort lifespan. The plot shows a weak positive association between deceleration factor estimates and median lifespan estimates from control cohorts. Each point represents one of the 22 comparisons listed in .
3.
Quantile-Quantile plots. Survival time quantiles calculated from control cohort survival times are plotted against corresponding survival time quantiles calculated from experimental cohort survival times. Part (A) shows a QQ plot for the Pit1(dw/dw) comparison and part (B) shows a QQ plot for the Clk1(+/−)(S2) comparison. The solid line represents a least-square regression line. Part (A) indicates that the AFT model appropriately describes the treatment effect for the Pit1(dw/dw) comparison, since points approximate a straight line. Part (B) suggests that the AFT model may not be appropriate for the Clk1(+/−)(S2) comparison, since points do not approximate a straight line. QQ plots for all 22 comparisons are shown in .
4.
Quantile regression estimation of treatment effects. Quantile regression was used to estimate treatment effects across a range of survival time quantiles (τ = 0.10,…,0.90). For a given quantile τ (horizontal axis), the vertical axis represents the percent increase in survivorship associated with an experimental treatment (). In part (A), results for the PappA(−/−) treatment are shown, and in part (B), results for the bIrs2(+/−) treatment are shown. In each plot, the middle line represents the calculated effect of experimental treatments at each survival time quantile, while the upper and lower lines outline a 95% confidence region ().
5.
Log-cumulative hazard plots. Part (A) shows a log-cumulative hazard plot for the Prop1(df/df) comparison, while part (B) shows the log-cumulative hazard plot for the Surf1(−/−) comparison. In both (A) and (B), the dotted line represents the logarithm of the estimated hazard function for the experimental treatment, while the solid line represents the logarithm of the estimated hazard function for the control treatment. Part (A) shows that, for the Prop1(df/df) comparison, the difference between log-hazard functions of control and experimental treatments is roughly consistent over time (as assumed by the PH model). Part (B) shows that, for the Surf1(−/−) comparison, the difference between log-hazard functions of control and experimental treatments varies over time, which suggests that the standard PH model may not be appropriate. Log-cumulative hazard plots for each of the 22 comparisons are shown in .
6.
AFT model deceleration factor estimates. The deceleration factor represents the parameter c in the relation S1 (ct) = S0(t), where S1 (t) is the survivorship of the experimental cohort at time t and S0 (t) represents survivorship of the control cohort at time t (). The value of 100(c − 1) provides an estimate of the percent treatment difference in lifespan (experimental versus control) for any survival time quantile. For each comparison (see ), filled symbols indicate the estimated deceleration factor value and bars represent a 95% confidence interval. Some deceleration factor estimates have been adjusted for covariates such as parental IDs, date of birth or gender (see ).
William R. Swindell. Exp Gerontol. ;44(3):190-200.
CitationFull text
Abstract
Aging, which affects all organ systems, is one of the most complex phenotypes. Recent discoveries in long-lived mutant mice have revealed molecular mechanisms of longevity in mammals which may contribute to our understanding of why humans age. These mutations include naturally occurring spontaneous mutations, and those of mice genetically modified by modern genomic technologies. It is generally believed that the most fundamental mechanisms of aging are evolutionarily conserved across species. The following types of longevity mechanisms have been intensively studied: suppression of the somatotropic (growth hormone/insulin-like growth factor 1) axis, decreased metabolism and increased resistance of oxidative stress, reduced insulin secretion and increased insulin sensitivity, and delayed reproductive maturation and reduced fertility. In addition, many of the mutations have a sex-dependent effect on lifespan, and when present in different genetic backgrounds, the effects of the same gene mutation can vary considerably. The present review discusses these phenotypic variations as well as describing the known longevity genes in long-lived mutant mice and the molecular mechanisms specifying longevity. We anticipate that these mouse studies will ultimately provide clues about how to delay the aging and prolong lifespan, and help to develop therapies for healthier human aging.
Copyright © 2010 Elsevier B.V. All rights reserved.
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