CH 591.pdf - Google Drive
Notes: From Sullivan Square, take the 95 bus (please check with driver to make sure the bus is going to Assembly Square). Get off at the stop after Home Depot. If you take the 95 bus from Medford, it lets you off on the wrong side of the highway. You have to go to Sullivan and take the 95 outbound.
CH 591.pdf - Google Drive
Carrier mobility, another essential parameter, represents how easily carriers move in materials under the action of the electric field. It reflects the sensitivity of the source-drain current to gate voltage. Mobility is firstly affected by the intrinsic electrical properties of the material. Generally, the theoretical phonon-limit mobility is the highest mobility that can be reached by the material. Although this predicts the potential of 2D semiconductors itself47, the actual situation of the device is more complex and cannot be generalized48. Various scattering factors, such as lattice defects, ionized impurities, grain boundaries, and other structural defects, are the major constraints that are often difficult to separate their respective contributions49. The influence of extrinsic effects caused by impurities and dielectric environments hinders the study of the inherent physics of 2D semiconductors and limits the design of devices. The theoretically predicted high mobility could not be obtained in the experiments. vdW heterojunction encapsulated by hBN and contacted by graphene can be measured for intrinsic mobility without the influence of external scattering, whereas this is not practical for actual transistor applications50. Mobility as an indirectly derived parameter is susceptible to some underlying deviations due to the test method and may not reflect the real state of the device12. In transistors with ultra-short channels, the carrier transport will be close to the ballistic transport and the effective drive current is more determined by the contact and dielectric environment.
Permits for Parking ChangesA permit for parking lots is required for any work that changes the configuration of existing off-street parking or adding new off-street parking. This includes, but is not limited to, modifying or adding new drive aisles or driveways, reconfiguring existing parking stalls/restriping, adding or removing parking stalls, resurfacing or installing new pavement, and altering pedestrian walkways or access to and from a parking area.
Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development.
Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation.
Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at -cancer-genes.org/.
Genetic alterations conferring selective advantages to cancer cells are the main drivers of cancer evolution and hunting for them has been at the core of international cancer genomic efforts [1,2,3]. Given the instability of the cancer genome, distinguishing driver alterations from the rest relies on analytical approaches that identify genes altered more frequently than expected or quantify the positive selection acting on them [4,5,6]. The results of these analyses have greatly expanded our understanding of the mechanisms driving cancer evolution, revealing high heterogeneity across and within cancers [7,8,9].
Recently, deep sequencing screens of non-cancer tissues have started to map positively selected genetic mutations in somatic cells that drive in situ formation of phenotypically normal clones [10, 11]. Many of these mutations hit cancer drivers, sometimes at a frequency higher than the corresponding cancer [12,13,14,15,16]. Yet, they do not drive malignant transformation. This conundrum poses fundamental questions on how genetic drivers of normal somatic evolution are related to and differ from those of cancer evolution. Addressing these questions will clarify the genetic relationship between tissue homeostasis and cancer initiation, with profound implications for cancer early detection.
To assess the extent of the current knowledge on cancer and non-cancer drivers, we undertook a systematic review of the literature and assembled a comprehensive repertoire of genes whose somatic alterations have been reported to drive cancer or non-cancer evolution. This allowed us to compare the current driver repertoire across and within cancer and non-cancer tissues and map their alterations in the large pancancer collection of samples from The Cancer Genome Atlas (TCGA). This revealed significant gaps and biases in our current knowledge of the driver landscape. We also computed an array of systems-level properties across driver groups, confirming the unique evolutionary path of driver genes and their central role in the cell.
We conducted a census of currently known drivers through a comprehensive literature review of 331 scientific articles published between 2008 and 2020 describing somatically altered genes with a proven or predicted role in cancer or non-cancer somatic evolution (Fig. 1a). These publications included three sources of experimentally validated (canonical) cancer drivers, 311 sequencing screens of cancer (293) and non-cancer (18) tissues, and 17 pancancer studies (Additional file 1, Table S1). Each paper was assessed by at least two independent experts (Additional file 2, Fig. S1A-C) returning a total of 3355 drivers, 3347 in 122 cancer types and 95 in 12 non-cancer tissues, respectively (Fig. 1a). We further computed the systems-level properties of drivers and annotated their function, somatic variation, and drug interactions (Fig. 1a).
Collection of a comprehensive repertoire of cancer and healthy drivers. a Literature review and driver annotation workflow. Expert literature curation of 331 publications led to a repertoire of cancer and healthy drivers in a variety of cancer and non-cancer tissues. Combining multiple data sources, a set of properties and annotations was computed for all these drivers. b Intersection of canonical drivers from three sources [17,18,19] that passed our manual curation. c Classification of canonical cancer drivers in tumor suppressors and oncogenes. Eighty-one cancer drivers had a dual role or could not be classified. d Intersection of canonical and candidate driver genes from 310 sequencing screens. Genes whose driver role had only statistical support were considered candidate cancer drivers. e Intersection between cancer drivers with coding and non-coding alterations. f Level of support for the driver role of 531 cancer genes with non-coding driver alterations only. Level 1 means that the gene was predicted as a driver only in one cancer sequencing screen; levels 2, 3, and 4 mean that it was predicted by two, three, or four screens or that it had experimental support. Experimental support was gathered from the 19 publications reporting non-coding cancer drivers (Additional file 1, Table S1) and from the CNCDatabase  and included in vitro and in vivo experiments, modification of gene expression, and survival association. g Proportion of healthy drivers that are also canonical or candidate cancer drivers, classified as canonical and candidate healthy drivers, respectively
We extracted additional cancer drivers from the curation of 310 sequencing screens that applied a variety of statistical approaches (Additional file 2, Fig. S1 D) to identify cancer drivers among all altered genes. After removing possible false positives (Additional file 3, Table S2), the final list included 3177 cancer drivers, 2756 of which relied only on statistical support (candidate cancer drivers) and 421 were canonical drivers (Fig. 1d, Additional file 4, Table S3). Therefore, 170 canonical drivers have never been detected by any method, suggesting that they may elicit their role through non-mutational mechanisms or may fall below the detection limits of current approaches. Given the prevalence of cancer coding screens (Fig. 1a), only coding driver alterations have been reported for most genes (Fig. 1e) while 16% of them (531) were identified as drivers uniquely in non-coding screens. Since the prediction of drivers with non-coding alterations remains challenging, we further investigated the type of support that these genes had for their driver activity. The overwhelming majority of them (467 genes, 87%) have been predicted as drivers in only one screen. The remaining 64 genes are canonical drivers, have been predicted as drivers in multiple screens, or have additional experimental support for their driver activity (Fig. 1f).
Applying a similar approach (Additional file 2, Fig. S1 A-C), we reviewed 18 sequencing screens of healthy or diseased (non-cancer) tissues. They collectively reported 95 genes whose somatic alterations could drive non-malignant clone formation (healthy drivers). Interestingly, only eight of them were not cancer drivers (Fig. 1g, Additional file 4, Table S3), suggesting a high overlap between genetic drivers of cancer and non-cancer evolution. However, since many non-cancer screens only re-sequenced cancer genes or applied methods developed for cancer genomics (Additional file 2, Fig. S1E), this overlap may be overestimated. 041b061a72