In this task, you are given an abstract of article. Your task is to generate title for this article. Preferred titles are under thirty words.

[EX Q]: DNA replication stress promotes genome instability in cancer. However, the contribution of the replication stress response to the development of malignancies remains unresolved. The DNA replication stress response protein SMARCAL1 stabilizes DNA replication forks and prevents replication fork collapse, a cause of DNA breaks and apoptosis. While the fork regression/remodeling functions of SMARCAL1 have been investigated, its in vivo functions in replication stress and cancer are unclear. Using a gamma radiation (IR)-induced replication stress T-cell lymphoma mouse model, we observed a significant inhibition of lymphomagenesis in mice lacking one or both alleles of Smarcal1. Notably, a quarter of the Smarcal1-deficient mice did not develop tumors. Moreover, hematopoietic stem/progenitor cells (HSPCs) and developing thymocytes in Smarcal1-deficient mice showed increased DNA damage and apoptosis during the proliferation burst following IR and an impaired ability to repopulate the thymus after IR. Additionally, mice lacking Smarcal1 showed significant HSPC defects when challenged to respond to other replication stress stimuli. Thus, our data reveal the critical function of the DNA replication stress response and, specifically, Smarcal1 in hematopoietic cell survival and tumor development. Our results also provide important insight into the immunodeficiency observed in individuals with mutations in SMARCAL1 by suggesting that it is an HSPC defect.
[EX A]: Defective replication stress response inhibits lymphomagenesis and impairs lymphocyte reconstitution

[EX Q]: Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. ©️ 2016 by John Wiley & Sons, Inc.
[EX A]: Comparative Protein Structure Modeling Using MODELLER.

[EX Q]: Kras mutation is a common phenomenon in many human neoplasms. We aimed to assess the Kras mutational status along the histological continuum from normal ovaries to the development of benign, borderline and malignant ovarian mucinous neoplasms. We analyzed 41 cases of malignant, 10 cases of borderline, 7 cases of benign mucinous ovarian tumors and 7 cases of normal ovarian tissue. The prevalence of Kras mutations in the normal ovary was 0.00% (n=0/7), while the prevalence in benign, borderline and malignant mucinous neoplasms was 57.14% (n=4/7), 90.00% (n=9/10) and 75.61% (n=31/41), respectively. Multiple Kras mutations were detected in 6 cases of mucinous carcinoma, including 5 double mutations with G13D/V14I (n=1), G12V/G13S (n=1), G12D/G13S (n=3) and one triple mutation with A11V/G13N/V14I (n=1). We identified six cases with 3 novel Kras mutations not previously described in the COSMIC database, which included A11V (n=3) and V14I (n=2) in mucinous carcinomas, and A11T (n=1) in a mucinous borderline tumor. In conclusion, Kras mutation appears to be one of the imperative events in the ovarian mucinous adenoma-borderline tumor-carcinoma sequence, as increased numbers of Kras mutations have been shown to be the strongest predictor of unequivocal malignancy in ovarian mucinous neoplasms.
[EX A]:
Multipoint Kras oncogene mutations potentially indicate mucinous carcinoma on the entire spectrum of mucinous ovarian neoplasms