Full text
2,043 characters
· extracted from
oa-doi-fallback
· click to expand
Abstract
The spontaneous cancer regression process (SCR) a rather rare paradoxical natural phenomenon, shows that malignant tumors can episodically undergo complete permanent elimination without treatment, and thus may be used for gaining clues to incisive therapeutic innovations. SCR of malignant breast tumors occurs subclinically across human populations (∼22% rate), according to Scandinavian and Wisconsin Screening Registries (monitoring 0.33million and 2.95million population respectively). SCR occurs in discoid melanomas (∼12% rate). SCR process has high novelty with no toxicity nor recurrence, and duplicating SCR process is indeed clinically desirable. We aim to probe SCR using melanoma as illustrative pilot-study, and identify candidate leads that may mimic SCR on the tumor. We investigated molecular biological of melanoma, finding two reciprocal phases: spontaneous-progression and spontaneous-regression, and showed driver genes for melanoma SCR. Then, we pursued network pharmaco-informatics analysis. We found that spontaneous melanoma eradication shows an unexpected finding, in distinct contrast to the prevalent view that immunological processes and immunotherapy are critical for melanoma regression, than targeting DNA. We found that in contradistinction, it is DNA interference process that is primary route for melanoma regression, while anti-tumor immune activation is only secondarily needed. We observed that targeting two pathways (kinase-system, inositide-system) by inhibiting two genes, arrested melanoma-progression phase, and activated the reverse phase, melanoma-regression. We investigated the interactions of possible candidate leads, indicating significant therapeutic potency. Our findings underscore the high impact possibility for leveraging the anomalous phenomenon of spontaneous cancer regression as a general oncological principle for probing targeted therapeutic interventions, with substantial clinical potentials.
Competing Interest Statement
The authors have declared no competing interest.
Text is read by the "Ask this paper" AI Q&A widget below.
Extraction quality varies by source — PMC NXML preserves structure
cleanly, OA-HTML may include some navigation residue, and OA-PDF can
have broken hyphenation. The publisher copy
(via DOI)
is the canonical version.