Prognostic Factors in Colorectal Cancer Compton, Carolyn C; Fielding, L. Peter; Burgart, Lawrence J ...
Archives of pathology & laboratory medicine (1976),
07/2000, Letnik:
124, Številka:
7
Journal Article
Recenzirano
* Background.--Under the auspices of the College of American Pathologists, the current state of knowledge regarding pathologic prognostic factors (factors linked to outcome) and predictive factors ...(factors predicting response to therapy) in colorectal carcinoma was evaluated. A multidisciplinary group of clinical (including the disciplines of medical oncology, surgical oncology, and radiation oncology), pathologic, and statistical experts in colorectal cancer reviewed all relevant medical literature and stratified the reported prognostic factors into categories that reflected the strength of the published evidence demonstrating their prognostic value. Accordingly, the following categories of prognostic factors were defined. Category I includes factors definitively proven to be of prognostic import based on evidence from multiple statistically robust published trials and generally used in patient management. Category IIA includes factors extensively studied biologically and/or clinically and repeatedly shown to have prognostic value for outcome and/or predictive value for therapy that is of sufficient import to be included in the pathology report but that remains to be validated in statistically robust studies. Category IIB includes factors shown to be promising in multiple studies but lacking sufficient data for inclusion in category I or IIA. Category III includes factors not yet sufficiently studied to determine their prognostic value. Category IV includes factors well studied and shown to have no prognostic significance. Materials and Methods.--The medical literature was critically reviewed, and the analysis revealed specific points of variability in approach that prevented direct comparisons among published studies and compromised the quality of the collective data. Categories of variability recognized included the following: (1) methods of analysis, (2) interpretation of findings, (3) reporting of data, and (4) statistical evaluation. Additional points of variability within these categories were defined from the collective experience of the group. Reasons for the assignment of an individual prognostic factor to category I, II, III, or IV (categories defined by the level of scientific validation) were outlined with reference to the specific types of variability associated with the supportive data. For each factor and category of variability related to that factor, detailed recommendations for improvement were made. The recommendations were based on the following aims: (1) to increase the uniformity and completeness of pathologic evaluation of tumor specimens, (2) to enhance the quality of the data needed for definitive evaluation of the prognostic value of individual prognostic factors, and (3) ultimately, to improve patient care. Results and Conclusions.--Factors that were determined to merit inclusion in category I were as follows: the local extent of tumor assessed pathologically (the pT category of the TNM staging system of the American Joint Committee on Cancer and the Union Internationale Contre le Cancer AJCC/UICC); regional lymph node metastasis (the pN category of the TNM staging system); blood or lymphatic vessel invasion; residual tumor following surgery with curative intent (the R classification of the AJCC/UICC staging system), especially as it relates to positive surgical margins; and preoperative elevation of carcinoembryonic antigen elevation (a factor established by laboratory medicine methods rather than anatomic pathology). Factors in category IIA included the following: tumor grade, radial margin status (for resection specimens with nonperitonealized surfaces), and residual tumor in the resection specimen following neoadjuvant therapy (the ypTNM category of the TNM staging system of the AJCC/UICC). Factors in category lib included the following: histologic type, histologic features associated with microsatellite instability (MSI) (ie, host lymphoid response to tumor and medullary or mucinous histologic type), high degree of MSI (MSI-H), loss of heterozygosity at 18q (DCC gene allelic loss), and tumor border configuration (infiltrating vs pushing border). Factors grouped in category III included the following: DNA content, all other molecular markers except loss of heterozygosity 18q/DCC and MSI-H, perineural invasion, microvessel density, tumor cell-associated proteins or carbohydrates, peritumoral fibrosis, peritumoral inflammatory response, focal neuroendocrine differentiation, nuclear organizing regions, and proliferation indices. Category IV factors included tumor size and gross tumor configuration. This report records findings and recommendations of the consensus conference group, organized according to structural guidelines defined herein. (Arch Pathol Lab Med. 2000;124:979-994)
TNM staging alone does not accurately predict outcome in colon cancer (CC) patients who may be eligible for adjuvant chemotherapy. It is unknown to what extent the molecular markers microsatellite ...instability (MSI) and mutations in BRAF or KRAS improve prognostic estimation in multivariable models that include detailed clinicopathological annotation.
After imputation of missing at random data, a subset of patients accrued in phase 3 trials with adjuvant chemotherapy (n=3016)—N0147 (NCT00079274) and PETACC3 (NCT00026273)—was aggregated to construct multivariable Cox models for 5-year overall survival that were subsequently validated internally in the remaining clinical trial samples (n=1499), and also externally in different population cohorts of chemotherapy-treated (n=949) or -untreated (n=1080) CC patients, and an additional series without treatment annotation (n=782).
TNM staging, MSI and BRAFV600E mutation status remained independent prognostic factors in multivariable models across clinical trials cohorts and observational studies. Concordance indices increased from 0.61–0.68 in the TNM alone model to 0.63–0.71 in models with added molecular markers, 0.65–0.73 with clinicopathological features and 0.66–0.74 with all covariates. In validation cohorts with complete annotation, the integrated time-dependent AUC rose from 0.64 for the TNM alone model to 0.67 for models that included clinicopathological features, with or without molecular markers. In patient cohorts that received adjuvant chemotherapy, the relative proportion of variance explained (R2) by TNM, clinicopathological features and molecular markers was on an average 65%, 25% and 10%, respectively.
Incorporation of MSI, BRAFV600E and KRAS mutation status to overall survival models with TNM staging improves the ability to precisely prognosticate in stage II and III CC patients, but only modestly increases prediction accuracy in multivariable models that include clinicopathological features, particularly in chemotherapy-treated patients.
To remain competitive for federal and state funding, state cooperative extension services must proactively incorporate issues programming and performance-based budgeting. State major program (SMP) ...design teams, which provide linkages between clientele groups and the research base, must conduct needs assessments to adjust to this new atmosphere of accountability. A case study illustrates how one Florida SMP (FL107, vegetable production, harvest, handling and integrated pest management in Florida) restructured its design team to become more flexible and proactive to target a wider range of outcomes. While still in the implementation phase, this model has already resulted in improved communication within the organization, better addressing extension needs at county level while facilitating reporting at the state level.
A three-arm randomized phase III trial in advanced colorectal cancer patients was designed to test whether substitution of an equivalent dose of (1) l-leucovorin or (2) oral leucovorin would more ...effectively potentiate fluorouracil (5-FU) than standard intravenous (I.V.) (d,l)-leucovorin.
A total of 926 chemotherapy-naive patients participated. Patients received one of three treatments: (A) intensive-course 5-FU plus l-leucovorin with I.V. leucovorin (Immunex Corp, Seattle, WA) at 100 mg/m2 and I.V. 5-FU at 370 mg/m2; (B) intensive-course 5-FU plus oral (d,l)-leucovorin with oral leucovarin at 125 mg/m2 on hours 0, 1, 2, and 3 (total dose, 500 mg/m2) followed by 5-FU 370 mg/m2 on hour 4; or (C) intensive-course 5-FU plus I.V. (d,l)-leucovorin with I.V. leucovorin 200 mg/m2 and 5-FU 370 mg/m2. Drugs were administered daily for 5 consecutive days. Courses were repeated at 4 and 8 weeks, and every 5 weeks thereafter. Dosage was reduced for neutropenia, thrombocytopenia, diarrhea, stomatitis, and dermatitis.
Of 926 eligible patients, 756 have died. The overall response rate for patients with measurable disease was 32% (165 of 514). There were no differences between regimens in response rates (arm A, 28% 47 of 140; arm B, 34% 60 of 174; and arm C, 34% 58 of 170) or in survival. There have been nine possible chemotherapy-related fatalities. Grade III to IV toxic effects did not differ appreciably by arm and included stomatitis (12% to 14%), diarrhea (15% to 19%), nausea (7% to 9%), and vomiting (6% to 8%).
There was no difference in response, survival, or toxicity between these three different leucovorin formulations combined with 5-FU.
High-density SNP arrays for genome-wide assessment of allelic variation have made high resolution genetic characterization of crop germplasm feasible. A medium density array for apple, the IRSC 8K ...SNP array, has been successfully developed and used for screens of bi-parental populations. However, the number of robust and well-distributed markers contained on this array was not sufficient to perform genome-wide association analyses in wider germplasm sets, or Pedigree-Based Analysis at high precision, because of rapid decay of linkage disequilibrium. We describe the development of an Illumina Infinium array targeting 20K SNPs. The SNPs were predicted from re-sequencing data derived from the genomes of 13 Malus × domestica apple cultivars and one accession belonging to a crab apple species (M. micromalus). A pipeline for SNP selection was devised that avoided the pitfalls associated with the inclusion of paralogous sequence variants, supported the construction of robust multi-allelic SNP haploblocks and selected up to 11 entries within narrow genomic regions of ±5 kb, termed focal points (FPs). Broad genome coverage was attained by placing FPs at 1 cM intervals on a consensus genetic map, complementing them with FPs to enrich the ends of each of the chromosomes, and by bridging physical intervals greater than 400 Kbps. The selection also included ∼3.7K validated SNPs from the IRSC 8K array. The array has already been used in other studies where ∼15.8K SNP markers were mapped with an average of ∼6.8K SNPs per full-sib family. The newly developed array with its high density of polymorphic validated SNPs is expected to be of great utility for Pedigree-Based Analysis and Genomic Selection. It will also be a valuable tool to help dissect the genetic mechanisms controlling important fruit quality traits, and to aid the identification of marker-trait associations suitable for the application of Marker Assisted Selection in apple breeding programs.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
A high-throughput genotyping platform is needed to enable marker-assisted breeding in the allo-octoploid cultivated strawberry Fragaria × ananassa. Short-read sequences from one diploid and 19 ...octoploid accessions were aligned to the diploid Fragaria vesca 'Hawaii 4' reference genome to identify single nucleotide polymorphisms (SNPs) and indels for incorporation into a 90 K Affymetrix® Axiom® array. We report the development and preliminary evaluation of this array.
About 36 million sequence variants were identified in a 19 member, octoploid germplasm panel. Strategies and filtering pipelines were developed to identify and incorporate markers of several types: di-allelic SNPs (66.6%), multi-allelic SNPs (1.8%), indels (10.1%), and ploidy-reducing "haploSNPs" (11.7%). The remaining SNPs included those discovered in the diploid progenitor F. iinumae (3.9%), and speculative "codon-based" SNPs (5.9%). In genotyping 306 octoploid accessions, SNPs were assigned to six classes with Affymetrix's "SNPolisher" R package. The highest quality classes, PolyHigh Resolution (PHR), No Minor Homozygote (NMH), and Off-Target Variant (OTV) comprised 25%, 38%, and 1% of array markers, respectively. These markers were suitable for genetic studies as demonstrated in the full-sib family 'Holiday' × 'Korona' with the generation of a genetic linkage map consisting of 6,594 PHR SNPs evenly distributed across 28 chromosomes with an average density of approximately one marker per 0.5 cM, thus exceeding our goal of one marker per cM.
The Affymetrix IStraw90 Axiom array is the first high-throughput genotyping platform for cultivated strawberry and is commercially available to the worldwide scientific community. The array's high success rate is likely driven by the presence of naturally occurring variation in ploidy level within the nominally octoploid genome, and by effectiveness of the employed array design and ploidy-reducing strategies. This array enables genetic analyses including generation of high-density linkage maps, identification of quantitative trait loci for economically important traits, and genome-wide association studies, thus providing a basis for marker-assisted breeding in this high value crop.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Research on methods for studying time-to-event data (survival analysis) has been extensive in recent years. The basic model in use today represents the hazard function for an individual through a ...proportional hazards model (Cox, 1972). Typically, it is assumed that a covariate's effect on the hazard function is constant throughout the course of the study. In this paper we propose a method to allow for possible deviations from the standard Cox model, by allowing the effect of a covariate to vary over time. This method is based on a dynamic linear model. We present our method in terms of a Bayesian hierarchical model. We fit the model to the data using Markov chain Monte Carlo methods. Finally, we illustrate the approach with several examples.
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