Testing Adaptive Therapy Protocols Using Gemcitabine and Capecitabine in a Preclinical Model of Endocrine-Resistant Breast Cancer.

Adaptive therapy, an ecologically inspired approach to cancer treatment, aims to overcome resistance and reduce toxicity by leveraging competitive interactions between drug-sensitive and drug-resistant subclones, prioritizing patient survival and quality of life instead of killing the maximum number of cancer cells. In preparation for a clinical trial, we used endocrine-resistant MCF7 breast cancer to stimulate second-line therapy and tested adaptive therapy using capecitabine, gemcitabine, or their combination in a mouse xenograft model. Dose modulation adaptive therapy with capecitabine alone increased survival time relative to MTD but not statistically significantly (HR = 0.22, 95% CI = 0.043-1.1, p = 0.065). However, when we alternated the drugs in both dose modulation (HR = 0.11, 95% CI = 0.024-0.55, p = 0.007) and intermittent adaptive therapies, the survival time was significantly increased compared to high-dose combination therapy (HR = 0.07, 95% CI = 0.013-0.42, p = 0.003). Overall, the survival time increased with reduced dose for both single drugs (p < 0.01) and combined drugs (p < 0.001), resulting in tumors with fewer proliferation cells (p = 0.0026) and more apoptotic cells (p = 0.045) compared to high-dose therapy. Adaptive therapy favors slower-growing tumors and shows promise in two-drug alternating regimens instead of being combined.

Testing Adaptive Therapy Protocols using Gemcitabine and Capecitabine on a Mouse Model of Endocrine-Resistant Breast Cancer.

Highly effective cancer therapies often face limitations due to acquired resistance and toxicity. Adaptive therapy, an ecologically inspired approach, seeks to control therapeutic resistance and minimize toxicity by leveraging competitive interactions between drug-sensitive and drug-resistant subclones, prioritizing patient survival and quality of life over maximum cell kill. In preparation for a clinical trial in breast cancer, we used large populations of MCF7 cells to rapidly generate endocrine-resistance breast cancer cell line. We then mimicked second line therapy in ER+ breast cancers by treating the endocrine-resistant MCF7 cells in a mouse xenograft model to test adaptive therapy with capecitabine, gemcitabine, or the combination of those two drugs. Dose-modulation adaptive therapy with capecitabine alone increased survival time relative to MTD, but not statistically significant (HR: 0.22, 95% CI 0.043- 1.1 P = 0.065). However, when we alternated the drugs in both dose modulation (HR = 0.11, 95% CI: 0.024 - 0.55, P = 0.007) and intermittent adaptive therapies significantly increased survival time compared to high dose combination therapy (HR = 0.07, 95% CI: 0.013 - 0.42; P = 0.003). Overall, survival time increased with reduced dose for both single drugs (P < 0.01) and combined drugs (P < 0.001). Adaptive therapy protocols resulted in tumors with lower proportions of proliferating cells (P = 0.0026) and more apoptotic cells (P = 0.045). The results show that Adaptive therapy outperforms high-dose therapy in controlling endocrine-resistant breast cancer, favoring slower-growing tumors, and showing promise in two-drug alternating regimens.

Quantification of DNA repair capacity in whole blood of patients with head and neck cancer and healthy donors by comet assay.

Comet assay has been used to estimate cancer risk by quantification of DNA damage and repair in response to mutagen challenge. Our goal was to adopt best practices for the alkaline comet assay to measure DNA repair capacity of white blood cells in whole blood of patients with squamous cell carcinoma of the head and neck (HNSCC). The results show that initial damage by 10 Gy of gamma radiation expressed as percent DNA in comet tail was higher in stimulated lymphocytes (61.1+/-11.8) compared to whole blood (43.0+/-12.1) but subsequent repair was similar with comet tail of approximately 20% at 15 min and 13% at 45 min after exposure. Exposure of whole blood embedded in agarose from 5 to 10 Gy gamma radiation was followed by an approximately 70% repair of the DNA damage within 45 min with a faster repair phase in the first 15 min. Variability of the measurement was lower within repeated measurements of the same person compared to measurement of different healthy individuals. The repair during first 15 min was slower (p=0.01) in ex-/non-smokers (41.0+/-2.1%) compared to smokers (50.3+/-2.7%). This phase of repair was also slower (p=0.02) in HNSCC patients (36.8+/-2.1%) compared to controls matched on age and smoking (46.4+/-3.0%). The results of this pilot study suggest that quantification of repair in whole blood following a gamma radiation challenge is feasible. Additional method optimization would be helpful to improve the assay for a large population screening.

The role of protein binding in induction of apoptosis by phenethyl isothiocyanate and sulforaphane in human non-small lung cancer cells.

Induction of apoptosis underlies a mechanism for inhibiting tumorigenesis by phenethyl isothiocyanate (PEITC) and sulforaphane (SFN). However, the upstream events by which isothiocyanates (ITC) induce apoptosis have not been fully investigated. As electrophiles, ITCs could trigger apoptosis by binding to DNA or proteins or by inducing oxidative stress. To better understand the molecular mechanisms of apoptosis by ITCs, we examined, as a first step, the role of these events in human non-small lung cancer A549 cells. PEITC was a more potent inducer than SFN; it induced apoptosis at 20 micromol/L, whereas SFN induced at 40 micromol/L but not at 20 micromol/L. To study binding with cellular proteins and DNA, cells were treated with (14)C-ITCs; the initial protein binding by PEITC was almost 3-fold than that of SFN. The binding by PEITC increased with time, whereas binding by SFN remained low. Therefore, 4 h after incubation proteins became the predominant targets for PEITC with a 6-fold binding than that of SFN. To characterize the chemical nature of binding by the ITCs, we used bovine serum albumin (BSA) as a surrogate protein. PEITC also modified BSA covalently to a greater extent than SFN occurring exclusively at cysteine residues. Surprisingly, neither PEITC nor SFN bound to DNA or RNA at detectable levels or caused significant DNA strand breakage. The levels of oxidative damage in cells, measured as reactive oxygen species, 8-oxo-deoxyguanosine, and protein carbonyls formation, were greater in cells treated with SFN than PEITC. Because PEITC is a stronger inducer of apoptosis than SFN, these results indicate that direct covalent binding to cellular proteins is an important early event in the induction of apoptosis by the ITCs.

Enrichment of low molecular weight fraction of serum for MS analysis of peptides associated with hepatocellular carcinoma.

A challenging aspect of biomarker discovery in serum is the interference of abundant proteins with identification of disease-related proteins and peptides. This study describes enrichment of serum by denaturing ultrafiltration, which enables an efficient profiling and identification of peptides up to 5 kDa. We consistently detect several hundred peptide-peaks in MALDI-TOF and SELDI-TOF spectra of enriched serum. The sample preparation is fast and reproducible with an average CV for all 276 peaks in the MALDI-TOF spectrum of 11%. Compared to unenriched serum, the number of peaks in enriched spectra is 4 times higher at an S/N ratio of 5 and 20 times higher at an S/N ratio of 10. To demonstrate utility of the methods, we compared 20 enriched sera of patients with hepatocellular carcinoma (HCC) and 20 age-matched controls using MALDI-TOF. The comparison of 332 peaks at p < 0.001 identified 45 differentially abundant peaks that classified HCC with 90% accuracy in this small pilot study. Direct TOF/TOF sequencing of the most abundant peptide matches with high probability des-Ala-fibrinopeptide A. This study shows that enrichment of the low molecular weight fraction of serum facilitates an efficient discovery of peptides that could serve as biomarkers for detection of HCC as well as other diseases.

Analysis of mass spectral serum profiles for biomarker selection.

MOTIVATION: Mass spectrometric profiles of peptides and proteins obtained by current technologies are characterized by complex spectra, high dimensionality and substantial noise. These characteristics generate challenges in the discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. We present low-level methods for the processing of mass spectral data and a machine learning method that combines support vector machines, with particle swarm optimization for biomarker selection. RESULTS: The proposed method identified mass points that achieved high prediction accuracy in distinguishing liver cancer patients from healthy individuals in SELDI-QqTOF profiles of serum. AVAILABILITY: MATLAB scripts to implement the methods described in this paper are available from the HWR's lab website http://lombardi.georgetown.edu/labpage