A study of inter-individual differences in the DNA damage response

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, February 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 46-48).Agents that damage our DNA are omnipresent in our environment and inside our cells themselves. Left unrepaired, DNA damage can lead to premature aging, neurodegeneration and cancer. Humans have thus evolved intricate and widespread mechanisms to repair and manage this damage. These mechanisms-called the DNA damage response-often involve cell cycle arrest. Cell cycle arrest gives the cells precious extra time to utilize its diverse set of repair pathways. Among these is the homologous recombination pathway, which repairs DNA double-strand breaks. When the damage is deemed irreparable, a cell can choose to die: this allows for the maintenance of genomic integrity of the organism. Humans share 99.9% of the same genetic information. The remaining 0.1% is responsible for all genetic variations between individuals. This includes differences in disease susceptibility. In this study, we examined the inter-individual differences in the DNA damage response. To do so, we used a panel of twenty-four B lymphoblastoid cell lines derived from twenty-four healthy individuals of diverse ancestries. This panel had already been shown to display a broad range of sensitivity to several DNA damaging agents. We focused our attention on the alkylating agents temozolomide and methylnitronitrosoguanidine (MNNG). While MNNG has been extensively studied as a model DNA damaging drug, temozolomide is used in the clinic today to treat astrocytoma and glioblastomas. The two drugs are often referred to as functional analogues. We wanted to see if the cell lines' relative sensitivities to both drugs would be similar, which would support the analogy made between the drugs, or different, which would refute it. Furthermore, we measured the amounts of sister chromatid exchanges (SCEs) induced by temozolomide treatment to determine if the sensitivity measured by growth inhibition post-treatment was correlated with the amount of temozolomide-induced SCEs. For the cell lines tested, we found that the MNNG-induced sensitivity was similar to that induced by temozolomide. We also found a cell line in which temozolomide induced a large growth inhibition, all the while inducing no detectable SCEs.by Meriem Sefta.S.M

Caractérisation moléculaire et clinique complète du rétinoblastome

Retinoblastoma is a rare pediatric cancer of the developing retina. In high-income countries, survival rates near 100%; however, enucleation of the affected eye has to be performed in over 70% of patients. Knudson’s 1971 two-hit hypothesis led to the discovery that this cancer usually initiates after a bi-allelic loss of the RB1 gene. Despite this early finding, little is known about the other molecular underpinnings of retinoblastoma. For instance, few genome-wide studies have described the genetic and epigenetic characteristics of these tumors. Furthermore, there is still no clear consensus regarding this cancer’s cell of origin, or whether or not it is homogenous disease. In this study, we built a comprehensive molecular and clinical portrait of retinoblastoma. Several lines of evidence led us to conclude that retinoblastoma is in fact a heterogeneous disease, with two distinct subtypes. We first uncovered the subtypes through a strategy that coupled an independent component analysis (ICA) of tumor transcriptomes to tumor immunohistochemical stainings. Retinoblastomas of the first subtype, called “cone-like”, homogeneously display cone-like differentiation, while those of the second subtype, called “bivalent-type”, exhibit strong intratumoral heterogeneity, with areas of cone-like differentiation intertwined with areas of ganglion-like differentiation. Further analysis of the transcriptomic data, as well as of copy number alteration data revealed that both subtypes may rely on different pathways and oncogenes. We notably observed a quasi-systematic presence of MDM4 gains or MYCN amplifications in bivalent-type tumors. We next turned to retinoblastomas’ methylomes; these considerably varied between the subtypes. ICA allowed us to decompose this inter-subtype methylomic heterogeneity, which was found to go beyond methylation due to cone-like or ganglion-like differentiation. We next studied the tumors’ clinical data, and found that cone-like tumors are most often diagnosed in very young patients with exophytic tumor growth, while bivalent-type tumors are found in older patients with endophytic tumor growth. Furthermore, patients with germline inactivations of RB1 mostly developed cone-like retinoblastomas, indicating that these tumors may initiate earlier during retinal development. In the final part of our study, we performed whole exome sequencing of 74 tumor-normal pairs. Like many pediatric cancers, the tumors had very low background mutation rates (0.1 mutations per megabase). Recurrent somatic mutations were found in RB1, BCOR and ARID1A, and these genes were also found to be in minimal regions of chromosomal losses. Importantly, both inactivations often had very high allelic frequencies, indicating that these events occur very early on in retinoblastoma tumorigenesis.Taken together, our study outlines a first comprehensive genomic portrait of retinoblastomas, points to the existence of two distinct subtypes, and provides insights into the cells-or-origin and the molecular mechanisms underlying these subtypes.Le rétinoblastome est un cancer pédiatrique rare de la rétine en cours de développement. Si dans les pays développés, le taux de survie avoisine 100%, une énucléation de l’oeil atteint est cependant nécessaire dans plus de 70% des cas.En 1971, Knudson émit l’hypothèse des deux “hits”, qui permit de comprendre que le rétinoblastome s’initie généralement après une perte bi-allélique du gène RB1. Cependant, les autres mécanismes moléculaires qui régissent ce cancer restent depuis peu connus. Par exemple, peu d’études génomiques ont été conduites. Ainsi, la nature de la cellule d’origine, ainsi que la présence ou non d’une hétérogénéité intertumorale, font encore débat. Dans cette étude, nous avons dressé un portrait génomique et clinique complet du rétinoblastome; plusieurs observations ont montré qu’il s’agit bien d’une maladie hétérogène, avec deux sous-types distincts. Nous avons d’abord identifié les deux sous-types avec à une approche couplant une analyse en composantes indépendantes (ACI) de transcriptomes tumoraux avec des marquages immunohistochimiques. Les rétinoblastomes du premier sous-type, dits “cone-like” expriment uniformément des marqueurs de cônes, tandis que ceux du second sous-type, dits “bivalent-type”, ont une forte hétérogénéité intratumorale, avec un enchevêtrement de zones de différenciation ganglionnaire ou cône. Grâce à une étude plus approfondie des transcriptomes et de données d’altérations génomiques, nous avons ensuite montré que les sous-types dépendent de voies de signalisation et d’oncogènes différents. Les bivalent-type ont notamment une présence quasi-systématique de gains de MDM4 ou d’amplifications de MYCN. Nous nous sommes ensuite tournés vers les méthylomes des rétinoblastomes, et constaté une forte hétérogénéité entre les sous-types. Nous avons décomposé cette hétérogénéité grâce à une ACI, et constaté qu’elle n’était pas liée uniquement à la différenciation cône ou ganglion. Nous avons ensuite étudié les données cliniques de la cohorte, et constaté que les sous-types avaient des âges au diagnostic et des formes de croissance différents, les tumeurs cone-like se developpant généralement chez des patients jeunes avec des tumeurs exophytiques, et les bivalent-type chez des patients plus âgés avec des tumeurs endophytiques. De plus, les patients avec des inactivations constitutionnelles du gène RB1 développent majoritairement des tumeurs cone-like; les cone-like s’initieraient donc plus tôt durant le développement de la rétine. Nous avons finalement séquencé les exomes de 74 paires tumeur-normal. Les rétinoblastomes avaient un taux de mutations extrêmement faible (0.1 mutations par mégabase), comme beaucoup de cancers pédiatriques. Nous avons identifié des mutations somatiques récurrentes dans RB1, BCOR et ARID1A. Ces gènes se trouvaient de plus dans des régions minimales de pertes chromosomiques. Surtout, les inactivations des deux gènes avaient souvent de fortes fréquences alléliques. Ceci indique que ces inactivations ont lieu précocément dans la tumorigénèse. En conclusion, notre étude a permis de dresser un premier portrait génomique complet du rétinoblastome, a révélé l’existence de deux sous-types distincts, ainsi que fourni des indices quant à la cellule d’origine de chaque sous-type, et les mécanismes moléculaires les régissant

A deep learning model to predict RNA-Seq expression of tumours from whole slide images

International audienceDeep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis to prediction of treatment outcomes. These methods have also been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides has yet been performed. We show that HE2RNA, a model based on the integration of multiple data modes, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone, without expert annotation. Through its interpretable design, HE2RNA provides virtual spatialization of gene expression, as validated by CD3- and CD20-staining on an independent dataset. The transcriptomic representation learned by HE2RNA can also be transferred on other datasets, even of small size, to increase prediction performance for specific molecular phenotypes. We illustrate the use of this approach in clinical diagnosis purposes such as the identification of tumors with microsatellite instability

A Parent-of-Origin Effect Impacts the Phenotype in Low Penetrance Retinoblastoma Families Segregating the c.1981C>T/p.Arg661Trp Mutation of RB1

International audienceRetinoblastoma (Rb), the most common pediatric intraocular neoplasm, results from inactivation of both alleles of the RB1 tumor suppressor gene. The second allele is most commonly lost, as demonstrated by loss of heterozygosity studies. RB1 germline carriers usually develop bilateral tumors, but some Rb families display low penetrance and variable expressivity. In order to decipher the underlying mechanisms, 23 unrelated low penetrance pedigrees segregating the common c.1981C>T/p.Arg661Trp mutation and other low penetrance mutations were studied. In families segregating the c.1981C>T mutation, we demonstrated, for the first time, a correlation between the gender of the transmitting carrier and penetrance, as evidenced by Fisher’s exact test: the probability of being unaffected is 90.3% and 32.5% when the mutation is inherited from the mother and the father, respectively (p-value = 7.10−7). Interestingly, a similar correlation was observed in families segregating other low penetrance alleles. Consequently, we investigated the putative involvement of an imprinted, modifier gene in low penetrance Rb. We first ruled out a MED4-driven mechanism by MED4 methylation and expression analyses. We then focused on the differentially methylated CpG85 island located in intron 2 of RB1 and showing parent-of-origin-specific DNA methylation. This differential methylation promotes expression of the maternal c.1981C>T allele. We propose that the maternally inherited c.1981C>T/p.Arg661Trp allele retains sufficient tumor suppressor activity to prevent retinoblastoma development. In contrast, when the mutation is paternally transmitted, the low residual activity would mimic a null mutation and subsequently lead to retinoblastoma. This implies that the c.1981C>T mutation is not deleterious per se but needs to be destabilized in order to reach pRb haploinsufficiency and initiate tumorigenesis. We suggest that this phenomenon might be a general mechanism to explain phenotypic differences in low penetrance Rb families

Family F7 segregating the <i>RB1</i> c.1981C>T/p.Arg661Trp mutation.

<p>Genotype is provided for tested members as m/n for heterozygous carriers and n/n for homozygous wild-type. OC indicates obligate carriers. Blackened symbols: bilateral Rb; half-blackened symbols: unilateral Rb; dotted symbols: unaffected carriers; dashed symbols: deceased.</p

<i>RB1</i> allelic imbalance in family F5.

<p>The normalized SNaPshot cDNA ratio between the mutant and the wild type alleles are indicated below each carrier individual with corresponding SNaPshot results. The c.1981C>T/p.Arg661Trp mutant allele “T” is indicated in green and the wild type allele “C” is indicated in blue. Dotted symbols: unaffected carriers; half-blackened symbols: unilateral Rb.</p

Methylation analyses of <i>RB1</i> CpG islands using methylation array.

<p>X axis represents the position on chromosome 13. Y axis represents overall methylation level. CpG106 localizing in <i>RB1</i> promoter is shown in green, CpG42 is shown in pink and CpG85 is shown in blue. For each sample, multiple CpGs are located within an island and each dot represents a single result. A: Normal retina. CpG85 showing approximately 50% of methylation. B: Tumor sample. CpG85 displaying a hypermethylated profile.</p

Expression imbalance in 20 carriers of the c.1981C>T/p.Arg661Trp mutation.

<p>Transmission in family F5 is detailed <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005888#pgen.1005888.g003" target="_blank">Fig 3</a>. First degree relatives are indicated for the other families. See text for ratio calculation. (*) See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005888#pgen.1005888.g003" target="_blank">Fig 3</a>.</p

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients

International audienceThe SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach