癌症专题-Pan-cancer analysis project

What is the Pan-cancer analysis project ?

To gain analytical breadth—defining commonalities, differences and emergent themes across cancer types and organs of origin— TCGA launched the Pan-Cancer analysis project at a meeting held on 26–27 October 2012 in Santa Cruz, California. The Pan-Cancer project is a coordinated initiative whose goals are to assemble coherent, consistent TCGA data sets across tumor types, as well as across platforms, and then to analyze and interpret these data at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.

What are the contents of pan-cancer analysis project ?

Molecular profiling of single tumor types

That cancer is fundamentally a genomic disease is now well established. Early on, large numbers of oncogenes were identified using functional assays on genetic material from tumors in positive-selection systems, and a subset of tumor suppressor genes was identified by analyzing loss of heterozygosity. This disease-specific focus has identified novel oncogenic drivers and the genes contributing to functional change, has established definitions of molecular subtypes and has identified new biomarkers on the basis of genomic, transcriptomic, proteomic and epigenomic alterations. For example, we now view ductal breast cancer as a collection of distinct diseases whose major subtypes (for example, luminal A, luminal B, HER2 and basal-like) are managed differently in the clinic; The outcomes for metastatic melanoma have improved as a result of therapeutic targeting of BRAFV600 mutations.

Analysis across tumor types

Increased numbers of tumor sample data sets enhance the ability to detect and analyze molecular defects in cancers. For example, driver genes can be pinpointed more precisely by narrowing regions affected by amplification and deletion to smaller segments of the chromosome using data on recurrent events across tumor types. Indeed, a majority of the TCGA samples have distinct alterations not shared with other samples in their cohort. Despite the apparent uniqueness of each individual tumor in this regard, the set of molecular aberrations often integrates into known biological pathways that are shared by sets of tumor samples.

In other cases, rare somatic mutations can be implicated as drivers by aggregating events across tumor types to improve the detection of patterns, for example, hotspot mutations in DNA segments that encode particular protein domains, leading to the identification of potential new drug targets. The identification of more driver aberrations and acquired vulnerabilities for each individual tumor will undoubtedly boost personalized care.

Important similarities among tumor subtypes from different organs have already been identified. For example, TP53 mutations drive high-grade serous ovarian, serous endometrial and basal-like breast carcinomas, all of which share a global transcriptional signature involving the activation of similar oncogenic pathways. Similarly, ERBB2-HER2 is mutated and/or amplified in subsets of glioblastoma, gastric, serous endometrial, bladder and lung cancers. The result, at least in some cases, is responsiveness to HER2-targeted therapy, analogous to that previously observed for HER2-amplified breast cancer. Such examples illustrate the importance of developing a comprehensive perspective across tumors, independent of histopathologic diagnosis; shared molecular patterns will enable etiologic and therapeutic discoveries in one disease that can be applied to another. Importantly, integrative interpretation of the data will help identify how the consequences of mutations vary across tissues, with important therapeutic implications. Relatively rare cancers, such as childhood malignancies, in particular stand to benefit from such an approach.

What are the limitations of Pan-cancer analysis project ?

Different platform and updated technology

Several data integration challenges place unavoidable limitations on cross-tumor analysis at the current time. A key challenge is the integration of data that have been generated on different platforms or updates of the same platform, as technologies improve. In the Pan-Cancer studies, for example, there have been transitions to much higher density DNA methylation arrays, use of different exome capture technologies, addition of RNA sequencing to microarray-based RNA characterization and increases in the quality and number of antibodies available for reverse-phase proteomic arrays (RPPAs).

A series of analyses of batch effects has been carried out to assess systematic and platform specific biases (R. Akbani, personal communication). However, more work is needed to establish best practices for minimizing unwanted batch effects while preserving biological signals.

Quality of clinical data vary by cancer type

The nature and quality of available clinical data vary widely by cancer type. Differences in these data limit the ability to establish one-size-fits-all norms for the comparison of demographic information, histopathologic characterization, behavioral context and clinical outcomes. For example, the Pan-Cancer survival data are relatively robust for serous ovarian cancer because of its poor prognosis but are still immature for breast and endometrial cancers, as (thankfully) most patients with these cancers do better for longer periods of time.Certain data elements are routinely collected only when they are anticipated to be relevant (for example the smoking history of patients with lung, bladder and head and neck cancers).

viral etiologies

Clear viral etiologies have been identified in several solid tumor types, including head and neck cancer, cervical cancer, Kaposi’s sarcoma and hepatocellular carcinoma. However, a pan-cancer analysis of the infectious etiologies of other cancers could not be conducted at present because infection status was recorded for only some tumors and tumor types (as an optional data element).

tumor stage and grade

Tumor stage and grade are not easily comparable across different tumor types because, for good reason, each tumor type has its own system. This set of challenges to cross-tumor analysis highlights the fact that current clinical practice is largely conducted according to classification by tissue or organ.

Future directions

Despite these challenges, the collection of Pan-Cancer publications presented here represents a landmark in the continuing effort to understand the common and contrasting biologies of cancers from a molecular perspective. Still, major questions amenable to further cross-tumor investigations remain, and the techniques used to compare different tumors will undoubtedly improve with use, time and further collaborative efforts. Further increasing the number of samples per tumor type and the variety of tumor types will improve our ability to detect rare driver events in heterogeneous tumor samples.

The hope is that investigations across tumor type such as the Pan-Cancer project will ultimately inform clinical decision-making. We hope such studies will enable the discovery of novel therapeutic agents that can be tested clinically—perhaps in novel adaptive, biomarker based clinical trials that cross boundaries between tumor types.

参考内容:

The Cancer Genome Atlas Pan-Cancer analysis project