Long-term prognosis of young breast cancer patients (≤40 years) who did not receive adjuvant systemic treatment: protocol for the PARADIGM initiative cohort study

Prognostic value of histopathologic traits independent of stromal tumor-infiltrating lymphocyte levels in chemotherapy-naïve patients with triple-negative breast cancer

BACKGROUND

In the absence of prognostic biomarkers, most patients with early-stage triple-negative breast cancer (eTNBC) are treated with combination chemotherapy. The identification of biomarkers to select patients for whom treatment de-escalation or escalation could be considered remains an unmet need. We evaluated the prognostic value of histopathologic traits in a unique cohort of young, (neo)adjuvant chemotherapy-naïve patients with early-stage (stage I or II), node-negative TNBC and long-term follow-up, in relation to stromal tumor-infiltrating lymphocytes (sTILs) for which the prognostic value was recently reported.

MATERIALS AND METHODS

We studied all 485 patients with node-negative eTNBC from the population-based PARADIGM cohort which selected women aged <40 years diagnosed between 1989 and 2000. None of the patients had received (neo)adjuvant chemotherapy according to standard practice at the time. Associations between histopathologic traits and breast cancer-specific survival (BCSS) were analyzed with Cox proportional hazard models.

RESULTS

With a median follow-up of 20.0 years, an independent prognostic value for BCSS was observed for lymphovascular invasion (LVI) [adjusted (adj.) hazard ratio (HR) 2.35, 95% confidence interval (CI) 1.49-3.69], fibrotic focus (adj. HR 1.61, 95% CI 1.09-2.37) and sTILs (per 10% increment adj. HR 0.75, 95% CI 0.69-0.82). In the sTILs <30% subgroup, the presence of LVI resulted in a higher cumulative incidence of breast cancer death (at 20 years, 58%; 95% CI 41% to 72%) compared with when LVI was absent (at 20 years, 32%; 95% CI 26% to 39%). In the ≥75% sTILs subgroup, the presence of LVI might be associated with poor survival (HR 11.45, 95% CI 0.71-182.36, two deaths). We confirm the lack of prognostic value of androgen receptor expression and human epidermal growth factor receptor 2 -low status.

CONCLUSIONS

sTILs, LVI and fibrotic focus provide independent prognostic information in young women with node-negative eTNBC. Our results are of importance for the selection of patients for de-escalation and escalation trials.

Long-term outcomes of young, node-negative, chemotherapy-naïve, triple-negative breast cancer patients according to BRCA1 status

BACKGROUND

Due to the abundant usage of chemotherapy in young triple-negative breast cancer (TNBC) patients, the unbiased prognostic value of BRCA1-related biomarkers in this population remains unclear. In addition, whether BRCA1-related biomarkers modify the well-established prognostic value of stromal tumor-infiltrating lymphocytes (sTILs) is unknown. This study aimed to compare the outcomes of young, node-negative, chemotherapy-naïve TNBC patients according to BRCA1 status, taking sTILs into account.

METHODS

We included 485 Dutch women diagnosed with node-negative TNBC under age 40 between 1989 and 2000. During this period, these women were considered low-risk and did not receive chemotherapy. BRCA1 status, including pathogenic germline BRCA1 mutation (gBRCA1m), somatic BRCA1 mutation (sBRCA1m), and tumor BRCA1 promoter methylation (BRCA1-PM), was assessed using DNA from formalin-fixed paraffin-embedded tissue. sTILs were assessed according to the international guideline. Patients' outcomes were compared using Cox regression and competing risk models.

RESULTS

Among the 399 patients with BRCA1 status, 26.3% had a gBRCA1m, 5.3% had a sBRCA1m, 36.6% had tumor BRCA1-PM, and 31.8% had BRCA1-non-altered tumors. Compared to BRCA1-non-alteration, gBRCA1m was associated with worse overall survival (OS) from the fourth year after diagnosis (adjusted HR, 2.11; 95% CI, 1.18-3.75), and this association attenuated after adjustment for second primary tumors. Every 10% sTIL increment was associated with 16% higher OS (adjusted HR, 0.84; 95% CI, 0.78-0.90) in gBRCA1m, sBRCA1m, or BRCA1-non-altered patients and 31% higher OS in tumor BRCA1-PM patients. Among the 66 patients with tumor BRCA1-PM and ≥ 50% sTILs, we observed excellent 15-year OS (97.0%; 95% CI, 92.9-100%). Conversely, among the 61 patients with gBRCA1m and < 50% sTILs, we observed poor 15-year OS (50.8%; 95% CI, 39.7-65.0%). Furthermore, gBRCA1m was associated with higher (adjusted subdistribution HR, 4.04; 95% CI, 2.29-7.13) and tumor BRCA1-PM with lower (adjusted subdistribution HR, 0.42; 95% CI, 0.19-0.95) incidence of second primary tumors, compared to BRCA1-non-alteration.

CONCLUSIONS

Although both gBRCA1m and tumor BRCA1-PM alter BRCA1 gene transcription, they are associated with different outcomes in young, node-negative, chemotherapy-naïve TNBC patients. By combining sTILs and BRCA1 status for risk classification, we were able to identify potential subgroups in this population to intensify and optimize adjuvant treatment.

External validation and clinical utility assessment of PREDICT breast cancer prognostic model in young, systemic treatment-naïve women with node-negative breast cancer

BACKGROUND

The validity of the PREDICT breast cancer prognostic model is unclear for young patients without adjuvant systemic treatment. This study aimed to validate PREDICT and assess its clinical utility in young women with node-negative breast cancer who did not receive systemic treatment.

METHODS

We selected all women from the Netherlands Cancer Registry who were diagnosed with node-negative breast cancer under age 40 between 1989 and 2000, a period when adjuvant systemic treatment was not standard practice for women with node-negative disease. We evaluated the calibration and discrimination of PREDICT using the observed/expected (O/E) mortality ratio, and the area under the receiver operating characteristic curve (AUC), respectively. Additionally, we compared the potential clinical utility of PREDICT for selectively administering chemotherapy to the chemotherapy-to-all strategy using decision curve analysis at predefined thresholds.

RESULTS

A total of 2264 women with a median age at diagnosis of 36 years were included. Of them, 71.2% had estrogen receptor (ER)-positive tumors and 44.0% had grade 3 tumors. Median tumor size was 16 mm. PREDICT v2.2 underestimated 10-year all-cause mortality by 33% in all women (O/E ratio:1.33, 95%CI:1.22-1.43). Model discrimination was moderate overall (AUC10-year:0.65, 95%CI:0.62-0.68), and poor for women with ER-negative tumors (AUC10-year:0.56, 95%CI:0.51-0.62). Compared to the chemotherapy-to-all strategy, PREDICT only showed a slightly higher net benefit in women with ER-positive tumors, but not in women with ER-negative tumors.

CONCLUSIONS

PREDICT yields unreliable predictions for young women with node-negative breast cancer. Further model updates are needed before PREDICT can be routinely used in this patient subset.

Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images

Breast cancer tumor grade is strongly associated with patient survival. In current clinical practice, pathologists assign tumor grade after visual analysis of tissue specimens. However, different studies show significant inter-observer variation in breast cancer grading. Computer-based breast cancer grading methods have been proposed but only work on specifically selected tissue areas and/or require labor-intensive annotations to be applied to new datasets. In this study, we trained and evaluated a deep learning-based breast cancer grading model that works on whole-slide histopathology images. The model was developed using whole-slide images from 706 young (< 40 years) invasive breast cancer patients with corresponding tumor grade (low/intermediate vs. high), and its constituents nuclear grade, tubule formation and mitotic rate. The performance of the model was evaluated using Cohen's kappa on an independent test set of 686 patients using annotations by expert pathologists as ground truth. The predicted low/intermediate (n = 327) and high (n = 359) grade groups were used to perform survival analysis. The deep learning system distinguished low/intermediate versus high tumor grade with a Cohen's Kappa of 0.59 (80% accuracy) compared to expert pathologists. In subsequent survival analysis the two groups predicted by the system were found to have a significantly different overall survival (OS) and disease/recurrence-free survival (DRFS/RFS) (p < 0.05). Univariate Cox hazard regression analysis showed statistically significant hazard ratios (p < 0.05). After adjusting for clinicopathologic features and stratifying for molecular subtype the hazard ratios showed a trend but lost statistical significance for all endpoints. In conclusion, we developed a deep learning-based model for automated grading of breast cancer on whole-slide images. The model distinguishes between low/intermediate and high grade tumors and finds a trend in the survival of the two predicted groups.

Prognostic Value of Stromal Tumor-Infiltrating Lymphocytes in Young, Node-Negative, Triple-Negative Breast Cancer Patients Who Did Not Receive (neo)Adjuvant Systemic Therapy

PURPOSE

Triple-negative breast cancer (TNBC) is considered aggressive, and therefore, virtually all young patients with TNBC receive (neo)adjuvant chemotherapy. Increased stromal tumor-infiltrating lymphocytes (sTILs) have been associated with a favorable prognosis in TNBC. However, whether this association holds for patients who are node-negative (N0), young (< 40 years), and chemotherapy-naïve, and thus can be used for chemotherapy de-escalation strategies, is unknown.

METHODS

We selected all patients with N0 TNBC diagnosed between 1989 and 2000 from a Dutch population–based registry. Patients were age < 40 years at diagnosis and had not received (neo)adjuvant systemic therapy, as was standard practice at the time. Formalin-fixed paraffin-embedded blocks were retrieved (PALGA: Dutch Pathology Registry), and a pathology review including sTILs was performed. Patients were categorized according to sTILs (< 30%, 30%-75%, and ≥ 75%). Multivariable Cox regression was performed for overall survival, with or without sTILs as a covariate. Cumulative incidence of distant metastasis or death was analyzed in a competing risk model, with second primary tumors as competing risk.

RESULTS

sTILs were scored for 441 patients. High sTILs (≥ 75%; 21%) translated into an excellent prognosis with a 15-year cumulative incidence of a distant metastasis or death of only 2.1% (95% CI, 0 to 5.0), whereas low sTILs (< 30%; 52%) had an unfavorable prognosis with a 15-year cumulative incidence of a distant metastasis or death of 38.4% (32.1 to 44.6). In addition, every 10% increment of sTILs decreased the risk of death by 19% (adjusted hazard ratio: 0.81; 95% CI, 0.76 to 0.87), which are an independent predictor adding prognostic information to standard clinicopathologic variables (χ² = 46.7, P < .001).

CONCLUSION

Chemotherapy-naïve, young patients with N0 TNBC with high sTILs (≥ 75%) have an excellent long-term prognosis. Therefore, sTILs should be considered for prospective clinical trials investigating (neo)adjuvant chemotherapy de-escalation strategies.

Young cancer patients with TNBC and high sTILs have an excellent outcome, even without systemic treatment

International strategy in cancer epidemiology: Japan's involvement in global projects and future role

In recent years, collaboration among researchers in the field of cancer epidemiology has been accelerating in various forms. Here, we review recent trends in international collaborative research activities in the cancer epidemiology field in Japan. These include not only support for other countries with less developed cancer statistics infrastructures, but also large-scale compilations and international comparisons through collaborative studies, as well as integration with analytical epidemiology and clinical research. Formation of international cohort consortia and estimates of cancer and risk factors in each country have contributed to raising the skill levels of cancer epidemiologists as well as to expanding research networks and activities among cancer epidemiologists. Molecular and genome epidemiological studies on cancer have progressed over decades and these continue to increase in size and dimension. Application of evidence from this area in prevention is still underway and needs further effort. Japanese epidemiologists have great potential to assume international leadership roles by taking advantage of the uniqueness, originality and characteristics of Japanese cohorts.

Association of germline genetic variants with breast cancer-specific survival in patient subgroups defined by clinic-pathological variables related to tumor biology and type of systemic treatment

BACKGROUND

Given the high heterogeneity among breast tumors, associations between common germline genetic variants and survival that may exist within specific subgroups could go undetected in an unstratified set of breast cancer patients.

METHODS

We performed genome-wide association analyses within 15 subgroups of breast cancer patients based on prognostic factors, including hormone receptors, tumor grade, age, and type of systemic treatment. Analyses were based on 91,686 female patients of European ancestry from the Breast Cancer Association Consortium, including 7531 breast cancer-specific deaths over a median follow-up of 8.1 years. Cox regression was used to assess associations of common germline variants with 15-year and 5-year breast cancer-specific survival. We assessed the probability of these associations being true positives via the Bayesian false discovery probability (BFDP < 0.15).

RESULTS

Evidence of associations with breast cancer-specific survival was observed in three patient subgroups, with variant rs5934618 in patients with grade 3 tumors (15-year-hazard ratio (HR) [95% confidence interval (CI)] 1.32 [1.20, 1.45], P = 1.4E-08, BFDP = 0.01, per G allele); variant rs4679741 in patients with ER-positive tumors treated with endocrine therapy (15-year-HR [95% CI] 1.18 [1.11, 1.26], P = 1.6E-07, BFDP = 0.09, per G allele); variants rs1106333 (15-year-HR [95% CI] 1.68 [1.39,2.03], P = 5.6E-08, BFDP = 0.12, per A allele) and rs78754389 (5-year-HR [95% CI] 1.79 [1.46,2.20], P = 1.7E-08, BFDP = 0.07, per A allele), in patients with ER-negative tumors treated with chemotherapy.

CONCLUSIONS

We found evidence of four loci associated with breast cancer-specific survival within three patient subgroups. There was limited evidence for the existence of associations in other patient subgroups. However, the power for many subgroups is limited due to the low number of events. Even so, our results suggest that the impact of common germline genetic variants on breast cancer-specific survival might be limited.

Digital pathology in the time of corona

The 2020 COVID-19 crisis has had and will have many implications for healthcare, including pathology. Rising number of infections create staffing shortages and other hospital departments might require pathology employees to fill more urgent positions. Furthermore, lockdown measures and social distancing cause many people to work from home. During this crisis, it became clearer than ever what an asset digital diagnostics is to keep pathologists, residents, molecular biologists and pathology assistants engaged in the diagnostic process, allowing social distancing and a ‘need to be there’ on-the-premises policy, while working effectively from home. This paper provides an overview of our way of working during the 2020 COVID-19 crisis with emphasis on the virtues of digital pathology.

Being fully digital: perspective of a Dutch academic pathology laboratory

The introduction of fast and robust whole slide scanners has facilitated the implementation of ‘digital pathology’ with various uses, the final challenge being full digital diagnostics. In this article, we describe the implementation process of a fully digital workflow for primary diagnostics in 2015 at the University Medical Centre in Utrecht, The Netherlands, as one of the first laboratories going fully digital with a future‐proof complete digital archive. Furthermore, we evaluated the experience of the first 2 years of working with the system by pathologists and residents. The system was successfully implemented in 6 months, including a European tender procedure. Most pathologists and residents had high confidence in working fully digitally, the expertise areas lagging behind being paediatrics, haematopathology, and neuropathology. Reported limitations concerned recognition of microorganisms and mitoses. Neither the age of respondents nor the number of years of pathology experience was correlated with the confidence level regarding digital diagnostics. The ergonomics of digital diagnostics were better than those of traditional microscopy. In this article, we describe our experiences in implementing our fully digital primary diagnostics workflow, describing in depth the implementation steps undertaken, the interlocking components that are required for a fully functional digital pathology system (laboratory management, hospital information systems, data storage, and whole slide scanners), and the changes required in workflow and slide production.

Hierarchical clustering of activated proteins in the PI3K and MAPK pathways in ER-positive, HER2-negative breast cancer with potential therapeutic consequences

Background

The phosphatidylinositol-3-kinase (PI3K) and/or mitogen-activated protein kinase (MAPK) pathways are frequently activated in breast cancer which can result in antioestrogen resistance. Single protein markers failed to be introduced into clinical practice. We, therefore, aimed to find a better read-out of activation of the PI3K and MAPK pathways in ER+/HER2− breast cancer. Assessment of seven PI3K/MAPK proteins might better reflect pathway activation and distinguish patients without adjuvant tamoxifen benefit.

Methods

Tumour blocks were recollected from 293 primary postmenopausal ER+/HER2− breast cancer patients randomised between tamoxifen and no adjuvant therapy. PTEN, p-AKT(Thr308), p-AKT(Ser473), p-p70S6K, p-4EBP1, p-ERK1/2 and p-S6RP expression was assessed by immunohistochemistry followed by unsupervised hierarchical clustering. The primary endpoint was recurrence-free interval. Multivariate Cox models were used to assess tamoxifen benefit. A classification tool was developed based on protein expression profile.

Results

Subgroups were identified with preferentially activated (A) and preferentially not activated (N) proteins. Patients in group N derived significant benefit from tamoxifen (multivariate hazard ratio (HR) = 0.23, p = 0.000101), while patients from group A did not (multivariate HR = 1.37, p = 0.64), p for interaction 0.020. Our generated classification tool confirmed these results (p for interaction 0.024).

Conclusions

Hierarchical clustering of seven PI3K/MAPK proteins reflects pathway activation and can guide treatment decisions in primary ER+/HER2− postmenopausal breast cancer patients.