Modulation of DNA Damage Response by SAM and HD Domain Containing Deoxynucleoside Triphosphate Triphosphohydrolase (SAMHD1) Determines Prognosis and Treatment Efficacy in Different Solid Tumor Types

SAMHD1 is a deoxynucleotide triphosphate (dNTP) triphosphohydrolase with important roles in the control of cell proliferation and apoptosis, either through the regulation of intracellular dNTPs levels or the modulation of the DNA damage response. However, SAMHD1 role in cancer evolution is still unknown. We performed the first in-depth study of SAMHD1 role in advanced solid tumors, by analyzing samples of 128 patients treated with chemotherapy agents based on platinum derivatives and/or antimetabolites and developing novel in vitro knock-out models to explore the mechanisms driving SAMHD1 function in cancer. Low or no expression of SAMHD1 was associated with a positive prognosis in breast, ovarian and non-small cell lung cancer (NSCLC) cancer patients. A predictive value was associated to low-SAMHD1 expression in NSCLC and ovarian patients treated with antimetabolites in combination with platinum derivatives. In vitro, SAMHD1 knock-out cells showed increased γ-H2AX and apoptosis suggesting that SAMHD1 depletion induces DNA damage leading to cell death. In vitro treatment with platinum-derived drugs significantly enhanced γ-H2AX and apoptotic markers expression in knock-out cells, indicating a synergic effect of SAMHD1 depletion and platinum-based treatment. SAMHD1 expression represents a new strong prognostic and predictive biomarker in solid tumors and thus, modulation of SAMHD1 function may constitute a promising target for the improvement of cancer therapy.

Single-Cell Epigenomics Reveals Mechanisms of Cancer Progression

Cancer initiation is driven by the cooperation between genetic and epigenetic aberrations that disrupt gene regulatory programs critical to maintain specialized cellular functions. After initiation, cells acquire additional genetic and epigenetic alterations influenced by tumor-intrinsic and -extrinsic mechanisms, which increase intratumoral heterogeneity, reshape the cell's underlying gene regulatory network, and promote cancer evolution. Furthermore, environmental or therapeutic insults drive the selection of heterogeneous cell states, with implications for cancer initiation, maintenance, and drug resistance. The advancement of single-cell genomics has begun to uncover the full repertoire of chromatin and gene expression states (cell states) that exist within individual tumors. These single-cell analyses suggest that cells diversify in their regulatory states upon transformation by co-opting damage-induced and nonlineage regulatory programs that can lead to epigenomic plasticity. Here, we review these recent studies related to regulatory state changes in cancer progression and highlight the growing single-cell epigenomics toolkit poised to address unresolved questions in the field. Expected final online publication date for the Annual Review of Cancer Biology, Volume 6 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Role of ctDNA in Breast Cancer

Breast cancer is currently classified by immunohistochemistry. However, technological advances in the detection of circulating tumor DNA (ctDNA) have made new options available for diagnosis, classification, biological knowledge, and treatment selection. Breast cancer is a heterogeneous disease and ctDNA can accurately reflect this heterogeneity, allowing us to detect, monitor, and understand the evolution of the disease. Breast cancer patients have higher levels of circulating DNA than healthy subjects, and ctDNA can be used for different objectives at different timepoints of the disease, ranging from screening and early detection to monitoring for resistance mutations in advanced disease. In early breast cancer, ctDNA clearance has been associated with higher rates of complete pathological response after neoadjuvant treatment and with fewer recurrences after radical treatments. In metastatic disease, ctDNA can help select the optimal sequencing of treatments. In the future, thanks to new bioinformatics tools, the use of ctDNA in breast cancer will become more frequent, enhancing our knowledge of the biology of tumors. Moreover, deep learning algorithms may also be able to predict breast cancer evolution or treatment sensitivity. In the coming years, continued research and the improvement of liquid biopsy techniques will be key to the implementation of ctDNA analysis in routine clinical practice.

Retinoblastoma protein regulates carcinogen susceptibility at heterochromatic cancer driver loci

Carcinogenic insult, such as UV light exposure, creates DNA lesions that evolve into mutations if left unrepaired. These resulting mutations can contribute to carcinogenesis and drive malignant phenotypes. Susceptibility to carcinogens (i.e., the propensity to form a carcinogen-induced DNA lesion) is regulated by both genetic and epigenetic factors. Importantly, carcinogen susceptibility is a critical contributor to cancer mutagenesis. It is known that mutations can be prevented by tumor suppressor regulation of DNA damage response pathways; however, their roles carcinogen susceptibility have not yet been reported. In this study, we reveal that the retinoblastoma (RB1) tumor suppressor regulates UV susceptibility across broad regions of the genome. In particular, centromere and telomere-proximal regions exhibit significant increases in UV lesion susceptibility when RB1 is deleted. Several cancer-related genes are located within genomic regions of increased susceptibility, including telomerase reverse transcriptase, TERT, thereby accelerating mutagenic potential in cancers with RB1 pathway alterations. These findings reveal novel genome stability mechanisms of a tumor suppressor and uncover new pathways to accumulate mutations during cancer evolution.

Sensei: how many samples to tell a change in cell type abundance?

Cellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html.

Genome Chaos, Information Creation, and Cancer Emergence: Searching for New Frameworks on the 50th Anniversary of the “War on Cancer”

The year 2021 marks the 50th anniversary of the National Cancer Act, signed by President Nixon, which declared a national “war on cancer.” Powered by enormous financial support, this past half-century has witnessed remarkable progress in understanding the individual molecular mechanisms of cancer, primarily through the characterization of cancer genes and the phenotypes associated with their pathways. Despite millions of publications and the overwhelming volume data generated from the Cancer Genome Project, clinical benefits are still lacking. In fact, the massive, diverse data also unexpectedly challenge the current somatic gene mutation theory of cancer, as well as the initial rationales behind sequencing so many cancer samples. Therefore, what should we do next? Should we continue to sequence more samples and push for further molecular characterizations, or should we take a moment to pause and think about the biological meaning of the data we have, integrating new ideas in cancer biology? On this special anniversary, we implore that it is time for the latter. We review the Genome Architecture Theory, an alternative conceptual framework that departs from gene-based theories. Specifically, we discuss the relationship between genes, genomes, and information-based platforms for future cancer research. This discussion will reinforce some newly proposed concepts that are essential for advancing cancer research, including two-phased cancer evolution (which reconciles evolutionary contributions from karyotypes and genes), stress-induced genome chaos (which creates new system information essential for macroevolution), the evolutionary mechanism of cancer (which unifies diverse molecular mechanisms to create new karyotype coding during evolution), and cellular adaptation and cancer emergence (which explains why cancer exists in the first place). We hope that these ideas will usher in new genomic and evolutionary conceptual frameworks and strategies for the next 50 years of cancer research.

Patients’ Experience of Systemic Treatment of Hepatocellular Carcinoma: A Review of the Impact on Quality of Life

Quality of life (QoL) in oncology is an outcome becoming more and more important and relevant to explore. Some studies have demonstrated its prognostic impact in different cancers, such as colorectal, breast, and prostate cancers, but also in hepatocellular carcinoma (HCC). Different tools have been developed for assessing quality of life, some general, such as EORTC QLQ-C30, but also specific tools depending on cancer origin which seem to be more pertinent for patients. Systemic treatments and specific symptoms due to cancer evolution could decrease quality of life. For approval of new systemic treatments, authorities ask for benefit in terms of efficacy but also benefit in quality of life, which is crucial for patients. This review reports data about QoL in HCC, including specific tools used, impact of systemic treatments and prognosis for QoL for HCC patients. Management of adverse events is essential to enhance compliance with treatment and quality of life. Assessing quality of life in clinical trials appears quite systematic, but its application in clinical routine requires development.

Loss of USP28 and SPINT2 expression promotes cancer cell survival after whole genome doubling

Background Whole genome doubling is a frequent event during cancer evolution and shapes the cancer genome due to the occurrence of chromosomal instability. Yet, erroneously arising human tetraploid cells usually do not proliferate due to p53 activation that leads to CDKN1A expression, cell cycle arrest, senescence and/or apoptosis. Methods To uncover the barriers that block the proliferation of tetraploids, we performed a RNAi mediated genome-wide screen in a human colorectal cancer cell line (HCT116). Results We identified 140 genes whose depletion improved the survival of tetraploid cells and characterized in depth two of them: SPINT2 and USP28. We found that SPINT2 is a general regulator of CDKN1A transcription via histone acetylation. Using mass spectrometry and immunoprecipitation, we found that USP28 interacts with NuMA1 and affects centrosome clustering. Tetraploid cells accumulate DNA damage and loss of USP28 reduces checkpoint activation, thus facilitating their proliferation. Conclusions Our results indicate three aspects that contribute to the survival of tetraploid cells: (i) increased mitogenic signaling and reduced expression of cell cycle inhibitors, (ii) the ability to establish functional bipolar spindles and (iii) reduced DNA damage signaling.