Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction

Background and aims: Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging.

Materials and methods: The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: ‘semi-structured’ and ‘unstructured’. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry.

Results: The best result of 99.4% accuracy – which included only one semi-structured report predicted as unstructured – was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured.

Conclusions: These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought.

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.

Association Between Results of a Gene Expression Signature Assay and Recurrence-Free Interval in Patients With Stage II Colon Cancer in Cancer and Leukemia Group B 9581 (Alliance)

PURPOSE: Conventional staging methods are inadequate to identify patients with stage II colon cancer (CC) who are at high risk of recurrence after surgery with curative intent. ColDx is a gene expression, microarray-based assay shown to be independently prognostic for recurrence-free interval (RFI) and overall survival in CC. The objective of this study was to further validate ColDx using formalin-fixed, paraffin-embedded specimens collected as part of the Alliance phase III trial, C9581.

PATIENTS AND METHODS: C9581 evaluated edrecolomab versus observation in patients with stage II CC and reported no survival benefit. Under an initial case-cohort sampling design, a randomly selected subcohort (RS) comprised 514 patients from 901 eligible patients with available tissue. Forty-nine additional patients with recurrence events were included in the analysis. Final analysis comprised 393 patients: 360 RS (58 events) and 33 non-RS events. Risk status was determined for each patient by ColDx. The Self-Prentice method was used to test the association between the resulting ColDx risk score and RFI adjusting for standard prognostic variables.

RESULTS: Fifty-five percent of patients (216 of 393) were classified as high risk. After adjustment for prognostic variables that included mismatch repair (MMR) deficiency, ColDx high-risk patients exhibited significantly worse RFI (multivariable hazard ratio, 2.13; 95% CI, 1.3 to 3.5; P < .01). Age and MMR status were marginally significant. RFI at 5 years for patients classified as high risk was 82% (95% CI, 79% to 85%), compared with 91% (95% CI, 89% to 93%) for patients classified as low risk.

CONCLUSION: ColDx is associated with RFI in the C9581 subsample in the presence of other prognostic factors, including MMR deficiency. ColDx could be incorporated with the traditional clinical markers of risk to refine patient prognosis.

Frequency of mutations and polymorphisms in borderline ovarian tumors of known cancer genes.

Borderline ovarian tumors represent an understudied subset of ovarian tumors. Most studies investigating aberrations in borderline tumors have focused on KRAS/BRAF mutations. In this study, we conducted an extensive analysis of mutations and single-nucleotide polymorphisms (SNPs) in borderline ovarian tumors. Using the Sequenom MassArray platform, we investigated 160 mutations/polymorphisms in 33 genes involved in cell signaling, apoptosis, angiogenesis, cell cycle regulation and cellular senescence. Of 52 tumors analyzed, 33 were serous, 18 mucinous and 1 endometrioid. KRAS c.35G>A p.Gly12Asp mutations were detected in eight tumors (six serous and two mucinous), BRAF V600E mutations in two serous tumors, and PIK3CA H1047Y and PIK3CA E542K mutations in a serous and an endometrioid BOT, respectively. CTNNB1 mutation was detected in a serous tumor. Potentially functional polymorphisms were found in vascular endothelial growth factor (VEGF), ABCB1, FGFR2 and PHLPP2. VEGF polymorphisms were the most common and detected at four loci. PHLPP2 polymorphisms were more frequent in mucinous as compared with serous tumors (P=0.04), with allelic imbalance in one case. This study represents the largest and most comprehensive analysis of mutations and functional SNPs in borderline ovarian tumors to date. At least 25% of borderline ovarian tumors harbor somatic mutations associated with potential response to targeted therapeutics.

Multicenter randomized phase II clinical trial comparing neoadjuvant oxaliplatin, capecitabine, and preoperative radiotherapy with or without cetuximab followed by total mesorectal excision in patients with high-risk rectal cancer (EXPERT-C).

PURPOSE: To evaluate the addition of cetuximab to neoadjuvant chemotherapy before chemoradiotherapy in high-risk rectal cancer. PATIENTS AND METHODS: Patients with operable magnetic resonance imaging-defined high-risk rectal cancer received four cycles of capecitabine/oxaliplatin (CAPOX) followed by capecitabine chemoradiotherapy, surgery, and adjuvant CAPOX (four cycles) or the same regimen plus weekly cetuximab (CAPOX+C). The primary end point was complete response (CR; pathologic CR or, in patients not undergoing surgery, radiologic CR) in patients with KRAS/BRAF wild-type tumors. Secondary end points were radiologic response (RR), progression-free survival (PFS), overall survival (OS), and safety in the wild-type and overall populations and a molecular biomarker analysis. RESULTS: One hundred sixty-five eligible patients were randomly assigned. Ninety (60%) of 149 assessable tumors were KRAS or BRAF wild type (CAPOX, n = 44; CAPOX+C, n = 46), and in these patients, the addition of cetuximab did not improve the primary end point of CR (9% v 11%, respectively; P = 1.0; odds ratio, 1.22) or PFS (hazard ratio [HR], 0.65; P = .363). Cetuximab significantly improved RR (CAPOX v CAPOX+C: after chemotherapy, 51% v 71%, respectively; P = .038; after chemoradiation, 75% v 93%, respectively; P = .028) and OS (HR, 0.27; P = .034). Skin toxicity and diarrhea were more frequent in the CAPOX+C arm. CONCLUSION: Cetuximab led to a significant increase in RR and OS in patients with KRAS/BRAF wild-type rectal cancer, but the primary end point of improved CR was not met.

Refining the diagnosis and EGFR status of non-small cell lung carcinoma in biopsy and cytologic material, using a panel of mucin staining, TTF-1, cytokeratin 5/6, and P63, and EGFR mutation analysis.

INTRODUCTION: The dichotomization of non-small cell carcinoma (NSCLC) subtype into squamous (SQCC) and adenocarcinoma (ADC) has become important in recent years and is increasingly required with regard to management. The aim of this study was to determine the utility of a panel of commercially available antibodies in refining the diagnosis on small biopsies and also to determine whether cytologic material is suitable for somatic EGFR genotyping in a prospectively analyzed series of patients undergoing investigation for suspected lung cancer. METHODS: Thirty-two consecutive cases of NSCLC were first tested using a panel comprising cytokeratin 5/6, P63, thyroid transcription factor-1, 34betaE12, and a D-PAS stain for mucin, to determine their value in refining diagnosis of NSCLC. After this test phase, two further pathologists independently reviewed the cases using a refined panel that excluded 34betaE12 because of its low specificity for SQCC, and refinement of diagnosis and concordance were assessed. Ten cases of ADC, including eight derived from cytologic samples, were sent for EGFR mutation analysis. RESULTS: There was refinement of diagnosis in 65% of cases of NSCLC to either SQCC or ADC in the test phase. This included 10 of 13 cases where cell pellets had been prepared from transbronchial needle aspirates. Validation by two further pathologists with varying expertise in lung pathology confirmed increased refinement and concordance of diagnosis. All samples were adequate for analysis, and they all showed a wild-type EGFR genotype. CONCLUSION: A panel comprising cytokeratin 5/6, P63, thyroid transcription factor-1, and a D-PAS stain for mucin increases diagnostic accuracy and agreement between pathologists when faced with refining a diagnosis of NSCLC to SQCC or ADC. These small samples, even cell pellets derived from transbronchial needle aspirates, seem to be adequate for EGFR mutation analysis.